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Browse files- .lancedb/Cus_manual_.lance/_latest.manifest +0 -0
- .lancedb/Cus_manual_.lance/_transactions/0-68be5234-afdd-46a3-8d8f-f15d3ddf8d9f.txn +1 -0
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- .lancedb/Cus_manual_.lance/data/d98dfa8d-3da9-429d-9225-799f5c713440.lance +0 -0
- .ragatouille/colbert/indexes/cbseclass10index/0.codes.pt +3 -0
- .ragatouille/colbert/indexes/cbseclass10index/0.metadata.json +6 -0
- .ragatouille/colbert/indexes/cbseclass10index/0.residuals.pt +3 -0
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- README.md +12 -12
- app.py +437 -0
- backend/query_llm.py +161 -0
- backend/semantic_search.py +28 -0
- lancedb/Cus_manual_.lance/_latest.manifest +0 -0
- lancedb/Cus_manual_.lance/_transactions/0-68be5234-afdd-46a3-8d8f-f15d3ddf8d9f.txn +1 -0
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- lancedb/Cus_manual_.lance/data/d98dfa8d-3da9-429d-9225-799f5c713440.lance +0 -0
- logo.png +0 -0
- requirements.txt +9 -0
- templates/template.j2 +8 -0
- templates/template_html.j2 +102 -0
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.ragatouille/colbert/indexes/cbseclass10index/ivf.pid.pt
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"triples":"/future/u/okhattab/root/unit/experiments/2021.10/downstream.distillation.round2.2_score/round2.nway6.cosine.ib/examples.64.json",
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"collection":[
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[
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"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n3.1 In terms of Regulation 4 of the Project Import Regulations, 1986 (PIR), the basic requirement for \navailing the benefit of assessment under Tariff Heading No. 98.01 is that the importer should have \nentered into one or more contracts with the su ppliers of the goods for setting up a project. Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs.",
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"Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs. \n3.2 Regulation 5 provides in the manner of registering contracts, which is as follows: \n(i) Before any order is made by the proper officer of Customs permitting the clearance of the \ngoods for home consumption; \n(ii) In the case of goods cleared for hom e consumption without payment of duty subject to re -\nexport in respect of fairs, exhibitions, demonstrations, seminars, congresses and \nconferences, duly sponsored or approved by the Government of India or Trade fair Authority \nof India, as the case may be, b efore the date of payment of duty. \n3.3 To expedite registration, the importers are advised to submit the following documents along with \nthe application for registration: \n(i) Original deed of contract together with true copy thereof.",
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| 44 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n(ii) Industrial License and letter of intent, SSI Certificate granted by the appropriate authority \nwith a copy thereof. \n \n(iii) Original Import licence, if any, with a list of items showing the dimensions, specifications, \nquantity, quality, value of each item duly attested by the Licensing Authority and a copy \nthereof."
|
| 45 |
+
]
|
| 46 |
+
],
|
| 47 |
+
"queries":"/future/u/okhattab/data/MSMARCO/queries.train.tsv",
|
| 48 |
+
"index_name":"cbseclass10index",
|
| 49 |
+
"overwrite":false,
|
| 50 |
+
"root":".ragatouille/",
|
| 51 |
+
"experiment":"colbert",
|
| 52 |
+
"index_root":null,
|
| 53 |
+
"name":"2024-08/15/14.23.27",
|
| 54 |
+
"rank":0,
|
| 55 |
+
"nranks":1,
|
| 56 |
+
"amp":true,
|
| 57 |
+
"gpus":1,
|
| 58 |
+
"avoid_fork_if_possible":false
|
| 59 |
+
},
|
| 60 |
+
"num_chunks":1,
|
| 61 |
+
"num_partitions":4096,
|
| 62 |
+
"num_embeddings":202637,
|
| 63 |
+
"avg_doclen":124.4699017199,
|
| 64 |
+
"RAGatouille":{
|
| 65 |
+
"index_config":{
|
| 66 |
+
"index_type":"PLAID",
|
| 67 |
+
"index_name":"cbseclass10index"
|
| 68 |
+
}
|
| 69 |
+
}
|
| 70 |
+
}
|
.ragatouille/colbert/indexes/cbseclass10index/pid_docid_map.json
ADDED
|
@@ -0,0 +1,1630 @@
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| 1 |
+
{
|
| 2 |
+
"0":"cf1e05c6-b606-4586-9f97-3c560c23e459",
|
| 3 |
+
"1":"cf1e05c6-b606-4586-9f97-3c560c23e459",
|
| 4 |
+
"2":"f43c0878-fd1d-4db6-bbc8-6d2549055009",
|
| 5 |
+
"3":"78e6a9af-5bd8-4fcc-bcbb-848b3d4f2b36",
|
| 6 |
+
"4":"ad208b9f-07a7-4871-9689-7fa70ecbaa8d",
|
| 7 |
+
"5":"3a7b06e1-9878-4cfc-b014-80acd222800e",
|
| 8 |
+
"6":"c8464179-c0fa-48e1-b1af-0ff827dda7c4",
|
| 9 |
+
"7":"c8464179-c0fa-48e1-b1af-0ff827dda7c4",
|
| 10 |
+
"8":"c8464179-c0fa-48e1-b1af-0ff827dda7c4",
|
| 11 |
+
"9":"c08b5a0c-8423-46b2-8163-f64b24347593",
|
| 12 |
+
"10":"f5621990-388a-4b25-9211-013cf26627e9",
|
| 13 |
+
"11":"aa496f24-3216-4645-9204-ad04476ea8ae",
|
| 14 |
+
"12":"aa496f24-3216-4645-9204-ad04476ea8ae",
|
| 15 |
+
"13":"57bc8ff6-2101-4cea-8dd4-273191b9447a",
|
| 16 |
+
"14":"770d66ed-5049-40aa-9083-a8111541c073",
|
| 17 |
+
"15":"0d43dafc-6cbc-4862-bc71-e3a30477f062",
|
| 18 |
+
"16":"0d43dafc-6cbc-4862-bc71-e3a30477f062",
|
| 19 |
+
"17":"ae27b034-3bfd-4915-91ad-0d2ec86ecf64",
|
| 20 |
+
"18":"d51161be-e5d5-4964-b123-d36adae0b746",
|
| 21 |
+
"19":"cf3a2350-4682-41a9-9a80-e950add86e9a",
|
| 22 |
+
"20":"cd04a93f-858c-41e3-88ca-e14c3c13ab4d",
|
| 23 |
+
"21":"ab2ce6a3-31ff-4286-832f-d7db071c3b7b",
|
| 24 |
+
"22":"472db0cd-2a79-4256-8d9e-a3adf6fc9f72",
|
| 25 |
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| 1570 |
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| 1575 |
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| 1576 |
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| 1579 |
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| 1580 |
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| 1581 |
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| 1582 |
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| 1583 |
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| 1584 |
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| 1585 |
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| 1586 |
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| 1587 |
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| 1594 |
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| 1595 |
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"1593":"f09f8192-4908-4439-86a0-de4e4eef082c",
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| 1596 |
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"1594":"f239fe8d-e26e-429a-9633-adaa53c17cac",
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| 1597 |
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| 1598 |
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"1596":"04e7ac42-5962-4b28-8f40-d2b4a3955446",
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| 1600 |
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| 1601 |
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| 1602 |
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| 1603 |
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| 1604 |
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|
| 1605 |
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"1603":"d9039736-2a96-4c6e-8297-0204d5e4311d",
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"1604":"ea600058-37c4-458a-80ef-71ba686ebc39",
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| 1607 |
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"1605":"ea600058-37c4-458a-80ef-71ba686ebc39",
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| 1608 |
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"1606":"ea600058-37c4-458a-80ef-71ba686ebc39",
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| 1609 |
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"1607":"13414b83-5693-43c1-92cd-8419be4f2a62",
|
| 1610 |
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"1608":"33ad8686-20db-4f22-84ad-be7fa80c57be",
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| 1611 |
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"1609":"77e97178-e83b-4825-acaa-40f0db2aaf3c",
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"1611":"0e9f3eb5-b1ca-4fd7-a42b-e439cdfab60a",
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| 1614 |
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"1612":"015c6e3e-3f5f-4593-8ae7-25d6a43e7076",
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| 1615 |
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| 1616 |
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"1614":"9e9a50b8-a040-42d6-a623-f42f422125ff",
|
| 1617 |
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"1615":"da27c6cc-ba28-4305-a9e0-ee72796b7474",
|
| 1618 |
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"1616":"da27c6cc-ba28-4305-a9e0-ee72796b7474",
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| 1619 |
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"1617":"2ee1b966-1009-459c-bb95-c61cb3dc64d4",
|
| 1620 |
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"1618":"9cc88d26-b671-4d56-8bf9-5d3c18b14b16",
|
| 1621 |
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"1619":"2f898629-f1e2-4e6d-8648-73d2a1baea74",
|
| 1622 |
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"1620":"5029fdc5-9e13-4221-b888-2eb8764ede6c",
|
| 1623 |
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"1621":"40e6934c-e965-4586-8188-4cf9126e20f7",
|
| 1624 |
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"1622":"e3c302c6-6850-4a27-bc38-6cf2195d99b8",
|
| 1625 |
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"1623":"e3c302c6-6850-4a27-bc38-6cf2195d99b8",
|
| 1626 |
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"1624":"e117219e-a4b4-4f42-8859-d2c233daea0b",
|
| 1627 |
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"1625":"68fdc464-50f5-4e8f-bdd2-bca678ad6150",
|
| 1628 |
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"1626":"68fdc464-50f5-4e8f-bdd2-bca678ad6150",
|
| 1629 |
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"1627":"17dc8801-a1dc-486f-8ebf-814bc37467fb"
|
| 1630 |
+
}
|
.ragatouille/colbert/indexes/cbseclass10index/plan.json
ADDED
|
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"config": {
|
| 3 |
+
"query_token_id": "[unused0]",
|
| 4 |
+
"doc_token_id": "[unused1]",
|
| 5 |
+
"query_token": "[Q]",
|
| 6 |
+
"doc_token": "[D]",
|
| 7 |
+
"ncells": null,
|
| 8 |
+
"centroid_score_threshold": null,
|
| 9 |
+
"ndocs": null,
|
| 10 |
+
"load_index_with_mmap": false,
|
| 11 |
+
"index_path": null,
|
| 12 |
+
"index_bsize": 32,
|
| 13 |
+
"nbits": 8,
|
| 14 |
+
"kmeans_niters": 20,
|
| 15 |
+
"resume": false,
|
| 16 |
+
"similarity": "cosine",
|
| 17 |
+
"bsize": 64,
|
| 18 |
+
"accumsteps": 1,
|
| 19 |
+
"lr": 1e-5,
|
| 20 |
+
"maxsteps": 400000,
|
| 21 |
+
"save_every": null,
|
| 22 |
+
"warmup": 20000,
|
| 23 |
+
"warmup_bert": null,
|
| 24 |
+
"relu": false,
|
| 25 |
+
"nway": 64,
|
| 26 |
+
"use_ib_negatives": true,
|
| 27 |
+
"reranker": false,
|
| 28 |
+
"distillation_alpha": 1.0,
|
| 29 |
+
"ignore_scores": false,
|
| 30 |
+
"model_name": null,
|
| 31 |
+
"query_maxlen": 32,
|
| 32 |
+
"attend_to_mask_tokens": false,
|
| 33 |
+
"interaction": "colbert",
|
| 34 |
+
"dim": 128,
|
| 35 |
+
"doc_maxlen": 256,
|
| 36 |
+
"mask_punctuation": true,
|
| 37 |
+
"checkpoint": "colbert-ir\/colbertv2.0",
|
| 38 |
+
"triples": "\/future\/u\/okhattab\/root\/unit\/experiments\/2021.10\/downstream.distillation.round2.2_score\/round2.nway6.cosine.ib\/examples.64.json",
|
| 39 |
+
"collection": [
|
| 40 |
+
"list with 1628 elements starting with...",
|
| 41 |
+
[
|
| 42 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n3.1 In terms of Regulation 4 of the Project Import Regulations, 1986 (PIR), the basic requirement for \navailing the benefit of assessment under Tariff Heading No. 98.01 is that the importer should have \nentered into one or more contracts with the su ppliers of the goods for setting up a project. Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs.",
|
| 43 |
+
"Such \ncontracts should be registered, prior to clearance, in the Custom House through which the goods \nare expected to be imported. The importer shall apply for such registration in writing to the proper \nofficer of Customs. \n3.2 Regulation 5 provides in the manner of registering contracts, which is as follows: \n(i) Before any order is made by the proper officer of Customs permitting the clearance of the \ngoods for home consumption; \n(ii) In the case of goods cleared for hom e consumption without payment of duty subject to re -\nexport in respect of fairs, exhibitions, demonstrations, seminars, congresses and \nconferences, duly sponsored or approved by the Government of India or Trade fair Authority \nof India, as the case may be, b efore the date of payment of duty. \n3.3 To expedite registration, the importers are advised to submit the following documents along with \nthe application for registration: \n(i) Original deed of contract together with true copy thereof.",
|
| 44 |
+
"Chapter 5 - Classification_Assessment of Projects Imports_ - Para 4 - _4. Clearance of goods after registration_.txt\n(ii) Industrial License and letter of intent, SSI Certificate granted by the appropriate authority \nwith a copy thereof. \n \n(iii) Original Import licence, if any, with a list of items showing the dimensions, specifications, \nquantity, quality, value of each item duly attested by the Licensing Authority and a copy \nthereof."
|
| 45 |
+
]
|
| 46 |
+
],
|
| 47 |
+
"queries": "\/future\/u\/okhattab\/data\/MSMARCO\/queries.train.tsv",
|
| 48 |
+
"index_name": "cbseclass10index",
|
| 49 |
+
"overwrite": false,
|
| 50 |
+
"root": ".ragatouille\/",
|
| 51 |
+
"experiment": "colbert",
|
| 52 |
+
"index_root": null,
|
| 53 |
+
"name": "2024-08\/15\/14.23.27",
|
| 54 |
+
"rank": 0,
|
| 55 |
+
"nranks": 1,
|
| 56 |
+
"amp": true,
|
| 57 |
+
"gpus": 1,
|
| 58 |
+
"avoid_fork_if_possible": false
|
| 59 |
+
},
|
| 60 |
+
"num_chunks": 1,
|
| 61 |
+
"num_partitions": 4096,
|
| 62 |
+
"num_embeddings_est": 202637.00051879883,
|
| 63 |
+
"avg_doclen_est": 124.46990203857422
|
| 64 |
+
}
|
README.md
CHANGED
|
@@ -1,12 +1,12 @@
|
|
| 1 |
-
---
|
| 2 |
-
title:
|
| 3 |
-
emoji:
|
| 4 |
-
colorFrom:
|
| 5 |
-
colorTo: indigo
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 4.
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
| 1 |
+
---
|
| 2 |
+
title: trial sci
|
| 3 |
+
emoji: 📚
|
| 4 |
+
colorFrom: red
|
| 5 |
+
colorTo: indigo
|
| 6 |
+
sdk: gradio
|
| 7 |
+
sdk_version: 4.36.0
|
| 8 |
+
app_file: app.py
|
| 9 |
+
pinned: false
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
app.py
ADDED
|
@@ -0,0 +1,437 @@
|
|
|
|
|
|
|
|
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|
| 1 |
+
|
| 2 |
+
from ragatouille import RAGPretrainedModel
|
| 3 |
+
import subprocess
|
| 4 |
+
import json
|
| 5 |
+
import spaces
|
| 6 |
+
import firebase_admin
|
| 7 |
+
from firebase_admin import credentials, firestore
|
| 8 |
+
import logging
|
| 9 |
+
from pathlib import Path
|
| 10 |
+
from time import perf_counter
|
| 11 |
+
from datetime import datetime
|
| 12 |
+
import gradio as gr
|
| 13 |
+
from jinja2 import Environment, FileSystemLoader
|
| 14 |
+
import numpy as np
|
| 15 |
+
from sentence_transformers import CrossEncoder
|
| 16 |
+
from huggingface_hub import InferenceClient
|
| 17 |
+
from os import getenv
|
| 18 |
+
|
| 19 |
+
from backend.query_llm import generate_hf, generate_openai
|
| 20 |
+
from backend.semantic_search import table, retriever
|
| 21 |
+
from huggingface_hub import InferenceClient
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
VECTOR_COLUMN_NAME = "vector"
|
| 25 |
+
TEXT_COLUMN_NAME = "text"
|
| 26 |
+
HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
|
| 27 |
+
proj_dir = Path(__file__).parent
|
| 28 |
+
# Setting up the logging
|
| 29 |
+
logging.basicConfig(level=logging.INFO)
|
| 30 |
+
logger = logging.getLogger(__name__)
|
| 31 |
+
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1",token=HF_TOKEN)
|
| 32 |
+
# Set up the template environment with the templates directory
|
| 33 |
+
env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))
|
| 34 |
+
|
| 35 |
+
# Load the templates directly from the environment
|
| 36 |
+
template = env.get_template('template.j2')
|
| 37 |
+
template_html = env.get_template('template_html.j2')
|
| 38 |
+
#___________________
|
| 39 |
+
# service_account_key='firebase.json'
|
| 40 |
+
# # Create a Certificate object from the service account info
|
| 41 |
+
# cred = credentials.Certificate(service_account_key)
|
| 42 |
+
# # Initialize the Firebase Admin
|
| 43 |
+
# firebase_admin.initialize_app(cred)
|
| 44 |
+
|
| 45 |
+
# # # Create a reference to the Firestore database
|
| 46 |
+
# db = firestore.client()
|
| 47 |
+
# #db usage
|
| 48 |
+
# collection_name = 'Nirvachana' # Replace with your collection name
|
| 49 |
+
# field_name = 'message_count' # Replace with your field name for count
|
| 50 |
+
# Examples
|
| 51 |
+
examples = ['Tabulate the difference between veins and arteries','What are defects in Human eye?',
|
| 52 |
+
'Frame 5 short questions and 5 MCQ on Chapter 2 ','Suggest creative and engaging ideas to teach students on Chapter on Metals and Non Metals '
|
| 53 |
+
]
|
| 54 |
+
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
# def get_and_increment_value_count(db , collection_name, field_name):
|
| 58 |
+
# """
|
| 59 |
+
# Retrieves a value count from the specified Firestore collection and field,
|
| 60 |
+
# increments it by 1, and updates the field with the new value."""
|
| 61 |
+
# collection_ref = db.collection(collection_name)
|
| 62 |
+
# doc_ref = collection_ref.document('count_doc') # Assuming a dedicated document for count
|
| 63 |
+
|
| 64 |
+
# # Use a transaction to ensure consistency across reads and writes
|
| 65 |
+
# try:
|
| 66 |
+
# with db.transaction() as transaction:
|
| 67 |
+
# # Get the current value count (or initialize to 0 if it doesn't exist)
|
| 68 |
+
# current_count_doc = doc_ref.get()
|
| 69 |
+
# current_count_data = current_count_doc.to_dict()
|
| 70 |
+
# if current_count_data:
|
| 71 |
+
# current_count = current_count_data.get(field_name, 0)
|
| 72 |
+
# else:
|
| 73 |
+
# current_count = 0
|
| 74 |
+
# # Increment the count
|
| 75 |
+
# new_count = current_count + 1
|
| 76 |
+
# # Update the document with the new count
|
| 77 |
+
# transaction.set(doc_ref, {field_name: new_count})
|
| 78 |
+
# return new_count
|
| 79 |
+
# except Exception as e:
|
| 80 |
+
# print(f"Error retrieving and updating value count: {e}")
|
| 81 |
+
# return None # Indicate error
|
| 82 |
+
|
| 83 |
+
# def update_count_html():
|
| 84 |
+
# usage_count = get_and_increment_value_count(db ,collection_name, field_name)
|
| 85 |
+
# ccount_html = gr.HTML(value=f"""
|
| 86 |
+
# <div style="display: flex; justify-content: flex-end;">
|
| 87 |
+
# <span style="font-weight: bold; color: maroon; font-size: 18px;">No of Usages:</span>
|
| 88 |
+
# <span style="font-weight: bold; color: maroon; font-size: 18px;">{usage_count}</span>
|
| 89 |
+
# </div>
|
| 90 |
+
# """)
|
| 91 |
+
# return count_html
|
| 92 |
+
|
| 93 |
+
# def store_message(db,query,answer,cross_encoder):
|
| 94 |
+
# timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
| 95 |
+
# # Create a new document reference with a dynamic document name based on timestamp
|
| 96 |
+
# new_completion= db.collection('Nirvachana').document(f"chatlogs_{timestamp}")
|
| 97 |
+
# new_completion.set({
|
| 98 |
+
# 'query': query,
|
| 99 |
+
# 'answer':answer,
|
| 100 |
+
# 'created_time': firestore.SERVER_TIMESTAMP,
|
| 101 |
+
# 'embedding': cross_encoder,
|
| 102 |
+
# 'title': 'Expenditure observer bot'
|
| 103 |
+
# })
|
| 104 |
+
|
| 105 |
+
|
| 106 |
+
def add_text(history, text):
|
| 107 |
+
history = [] if history is None else history
|
| 108 |
+
history = history + [(text, None)]
|
| 109 |
+
return history, gr.Textbox(value="", interactive=False)
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
def bot(history, cross_encoder):
|
| 113 |
+
top_rerank = 25
|
| 114 |
+
top_k_rank = 20
|
| 115 |
+
query = history[-1][0]
|
| 116 |
+
|
| 117 |
+
if not query:
|
| 118 |
+
gr.Warning("Please submit a non-empty string as a prompt")
|
| 119 |
+
raise ValueError("Empty string was submitted")
|
| 120 |
+
|
| 121 |
+
logger.warning('Retrieving documents...')
|
| 122 |
+
|
| 123 |
+
# if COLBERT RAGATATOUILLE PROCEDURE :
|
| 124 |
+
if cross_encoder=='(HIGH ACCURATE) ColBERT':
|
| 125 |
+
gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
|
| 126 |
+
RAG= RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
|
| 127 |
+
RAG_db=RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
|
| 128 |
+
documents_full=RAG_db.search(query,k=top_k_rank)
|
| 129 |
+
|
| 130 |
+
documents=[item['content'] for item in documents_full]
|
| 131 |
+
# Create Prompt
|
| 132 |
+
prompt = template.render(documents=documents, query=query)
|
| 133 |
+
prompt_html = template_html.render(documents=documents, query=query)
|
| 134 |
+
|
| 135 |
+
generate_fn = generate_hf
|
| 136 |
+
|
| 137 |
+
history[-1][1] = ""
|
| 138 |
+
for character in generate_fn(prompt, history[:-1]):
|
| 139 |
+
history[-1][1] = character
|
| 140 |
+
yield history, prompt_html
|
| 141 |
+
print('Final history is ',history)
|
| 142 |
+
#store_message(db,history[-1][0],history[-1][1],cross_encoder)
|
| 143 |
+
else:
|
| 144 |
+
# Retrieve documents relevant to query
|
| 145 |
+
document_start = perf_counter()
|
| 146 |
+
|
| 147 |
+
query_vec = retriever.encode(query)
|
| 148 |
+
logger.warning(f'Finished query vec')
|
| 149 |
+
doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
|
| 153 |
+
logger.warning(f'Finished search')
|
| 154 |
+
documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_rerank).to_list()
|
| 155 |
+
documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
|
| 156 |
+
logger.warning(f'start cross encoder {len(documents)}')
|
| 157 |
+
# Retrieve documents relevant to query
|
| 158 |
+
query_doc_pair = [[query, doc] for doc in documents]
|
| 159 |
+
if cross_encoder=='(FAST) MiniLM-L6v2' :
|
| 160 |
+
cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
|
| 161 |
+
elif cross_encoder=='(ACCURATE) BGE reranker':
|
| 162 |
+
cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
|
| 163 |
+
|
| 164 |
+
cross_scores = cross_encoder1.predict(query_doc_pair)
|
| 165 |
+
sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
|
| 166 |
+
logger.warning(f'Finished cross encoder {len(documents)}')
|
| 167 |
+
|
| 168 |
+
documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
|
| 169 |
+
logger.warning(f'num documents {len(documents)}')
|
| 170 |
+
|
| 171 |
+
document_time = perf_counter() - document_start
|
| 172 |
+
logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')
|
| 173 |
+
|
| 174 |
+
# Create Prompt
|
| 175 |
+
prompt = template.render(documents=documents, query=query)
|
| 176 |
+
prompt_html = template_html.render(documents=documents, query=query)
|
| 177 |
+
|
| 178 |
+
generate_fn = generate_hf
|
| 179 |
+
|
| 180 |
+
history[-1][1] = ""
|
| 181 |
+
for character in generate_fn(prompt, history[:-1]):
|
| 182 |
+
history[-1][1] = character
|
| 183 |
+
yield history, prompt_html
|
| 184 |
+
print('Final history is ',history)
|
| 185 |
+
#store_message(db,history[-1][0],history[-1][1],cross_encoder)
|
| 186 |
+
|
| 187 |
+
def system_instructions(question_difficulty, topic,documents_str):
|
| 188 |
+
return f"""<s> [INST] Your are a great teacher and your task is to create 10 questions with 4 choices with a {question_difficulty} difficulty about topic request " {topic} " only from the below given documents, {documents_str} then create an answers. Index in JSON format, the questions as "Q#":"" to "Q#":"", the four choices as "Q#:C1":"" to "Q#:C4":"", and the answers as "A#":"Q#:C#" to "A#":"Q#:C#". [/INST]"""
|
| 189 |
+
|
| 190 |
+
|
| 191 |
+
#with gr.Blocks(theme='Insuz/SimpleIndigo') as demo:
|
| 192 |
+
with gr.Blocks(theme='NoCrypt/miku') as CHATBOT:
|
| 193 |
+
with gr.Row():
|
| 194 |
+
with gr.Column(scale=10):
|
| 195 |
+
# gr.Markdown(
|
| 196 |
+
# """
|
| 197 |
+
# # Theme preview: `paris`
|
| 198 |
+
# To use this theme, set `theme='earneleh/paris'` in `gr.Blocks()` or `gr.Interface()`.
|
| 199 |
+
# You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
|
| 200 |
+
# of this theme.
|
| 201 |
+
# """
|
| 202 |
+
# )
|
| 203 |
+
gr.HTML(value="""<div style="color: #FF4500;"><h1>CHEERFULL CBSE-</h1> <h1><span style="color: #008000">AI Assisted Fun Learning</span></h1>
|
| 204 |
+
</div>""", elem_id='heading')
|
| 205 |
+
|
| 206 |
+
gr.HTML(value=f"""
|
| 207 |
+
<p style="font-family: sans-serif; font-size: 16px;">
|
| 208 |
+
A free Artificial Intelligence Chatbot assistant trained on CBSE Class 10 Science Notes to engage and help students and teachers of Puducherry.
|
| 209 |
+
</p>
|
| 210 |
+
""", elem_id='Sub-heading')
|
| 211 |
+
#usage_count = get_and_increment_value_count(db,collection_name, field_name)
|
| 212 |
+
gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;">Developed by K M Ramyasri , TGT,GHS.SUTHUKENY . Suggestions may be sent to <a href="mailto:[email protected]" style="color: #00008B; font-style: italic;">[email protected]</a>.</p>""", elem_id='Sub-heading1 ')
|
| 213 |
+
|
| 214 |
+
with gr.Column(scale=3):
|
| 215 |
+
gr.Image(value='logo.png',height=200,width=200)
|
| 216 |
+
|
| 217 |
+
|
| 218 |
+
# gr.HTML(value="""<div style="color: #FF4500;"><h1>CHEERFULL CBSE-</h1> <h1><span style="color: #008000">AI Assisted Fun Learning</span></h1>
|
| 219 |
+
# <img src='logo.png' alt="Chatbot" width="50" height="50" />
|
| 220 |
+
# </div>""", elem_id='heading')
|
| 221 |
+
|
| 222 |
+
# gr.HTML(value=f"""
|
| 223 |
+
# <p style="font-family: sans-serif; font-size: 16px;">
|
| 224 |
+
# A free Artificial Intelligence Chatbot assistant trained on CBSE Class 10 Science Notes to engage and help students and teachers of Puducherry.
|
| 225 |
+
# </p>
|
| 226 |
+
# """, elem_id='Sub-heading')
|
| 227 |
+
# #usage_count = get_and_increment_value_count(db,collection_name, field_name)
|
| 228 |
+
# gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 16px;">Developed by K M Ramyasri , PGT . Suggestions may be sent to <a href="mailto:[email protected]" style="color: #00008B; font-style: italic;">[email protected]</a>.</p>""", elem_id='Sub-heading1 ')
|
| 229 |
+
# # count_html = gr.HTML(value=f"""
|
| 230 |
+
# # <div style="display: flex; justify-content: flex-end;">
|
| 231 |
+
# # <span style="font-weight: bold; color: maroon; font-size: 18px;">No of Usages:</span>
|
| 232 |
+
# # <span style="font-weight: bold; color: maroon; font-size: 18px;">{usage_count}</span>
|
| 233 |
+
# # </div>
|
| 234 |
+
# # """)
|
| 235 |
+
|
| 236 |
+
chatbot = gr.Chatbot(
|
| 237 |
+
[],
|
| 238 |
+
elem_id="chatbot",
|
| 239 |
+
avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
|
| 240 |
+
'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
|
| 241 |
+
bubble_full_width=False,
|
| 242 |
+
show_copy_button=True,
|
| 243 |
+
show_share_button=True,
|
| 244 |
+
)
|
| 245 |
+
|
| 246 |
+
with gr.Row():
|
| 247 |
+
txt = gr.Textbox(
|
| 248 |
+
scale=3,
|
| 249 |
+
show_label=False,
|
| 250 |
+
placeholder="Enter text and press enter",
|
| 251 |
+
container=False,
|
| 252 |
+
)
|
| 253 |
+
txt_btn = gr.Button(value="Submit text", scale=1)
|
| 254 |
+
|
| 255 |
+
cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2','(ACCURATE) BGE reranker','(HIGH ACCURATE) ColBERT'], value='(ACCURATE) BGE reranker',label="Embeddings", info="Only First query to Colbert may take litte time)")
|
| 256 |
+
|
| 257 |
+
prompt_html = gr.HTML()
|
| 258 |
+
# Turn off interactivity while generating if you click
|
| 259 |
+
txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
|
| 260 |
+
bot, [chatbot, cross_encoder], [chatbot, prompt_html])#.then(update_count_html,[],[count_html])
|
| 261 |
+
|
| 262 |
+
# Turn it back on
|
| 263 |
+
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
|
| 264 |
+
|
| 265 |
+
# Turn off interactivity while generating if you hit enter
|
| 266 |
+
txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
|
| 267 |
+
bot, [chatbot, cross_encoder], [chatbot, prompt_html])#.then(update_count_html,[],[count_html])
|
| 268 |
+
|
| 269 |
+
# Turn it back on
|
| 270 |
+
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
|
| 271 |
+
|
| 272 |
+
# Examples
|
| 273 |
+
gr.Examples(examples, txt)
|
| 274 |
+
|
| 275 |
+
|
| 276 |
+
RAG_db=gr.State()
|
| 277 |
+
|
| 278 |
+
with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green"), css="style.css") as QUIZBOT:
|
| 279 |
+
def load_model():
|
| 280 |
+
RAG= RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
|
| 281 |
+
RAG_db.value=RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
|
| 282 |
+
return 'Ready to Go!!'
|
| 283 |
+
with gr.Column(scale=4):
|
| 284 |
+
gr.HTML("""
|
| 285 |
+
<center>
|
| 286 |
+
<h1><span style="color: purple;">AI NANBAN</span> - CBSE Class Quiz Maker</h1>
|
| 287 |
+
<h2>AI-powered Learning Game</h2>
|
| 288 |
+
<i>⚠️ Students create quiz from any topic /CBSE Chapter ! ⚠️</i>
|
| 289 |
+
</center>
|
| 290 |
+
""")
|
| 291 |
+
#gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
|
| 292 |
+
with gr.Column(scale=2):
|
| 293 |
+
load_btn = gr.Button("Click to Load!🚀")
|
| 294 |
+
load_text=gr.Textbox()
|
| 295 |
+
load_btn.click(load_model,[],load_text)
|
| 296 |
+
|
| 297 |
+
|
| 298 |
+
topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic from CBSE notes")
|
| 299 |
+
|
| 300 |
+
with gr.Row():
|
| 301 |
+
radio = gr.Radio(
|
| 302 |
+
["easy", "average", "hard"], label="How difficult should the quiz be?"
|
| 303 |
+
)
|
| 304 |
+
|
| 305 |
+
|
| 306 |
+
generate_quiz_btn = gr.Button("Generate Quiz!🚀")
|
| 307 |
+
quiz_msg=gr.Textbox()
|
| 308 |
+
|
| 309 |
+
question_radios = [gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(
|
| 310 |
+
visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(
|
| 311 |
+
visible=False), gr.Radio(visible=False), gr.Radio(visible=False)]
|
| 312 |
+
|
| 313 |
+
print(question_radios)
|
| 314 |
+
|
| 315 |
+
@spaces.GPU
|
| 316 |
+
@generate_quiz_btn.click(inputs=[radio, topic], outputs=[quiz_msg]+question_radios, api_name="generate_quiz")
|
| 317 |
+
def generate_quiz(question_difficulty, topic):
|
| 318 |
+
top_k_rank=10
|
| 319 |
+
RAG_db_=RAG_db.value
|
| 320 |
+
documents_full=RAG_db_.search(topic,k=top_k_rank)
|
| 321 |
+
|
| 322 |
+
|
| 323 |
+
|
| 324 |
+
generate_kwargs = dict(
|
| 325 |
+
temperature=0.2,
|
| 326 |
+
max_new_tokens=4000,
|
| 327 |
+
top_p=0.95,
|
| 328 |
+
repetition_penalty=1.0,
|
| 329 |
+
do_sample=True,
|
| 330 |
+
seed=42,
|
| 331 |
+
)
|
| 332 |
+
question_radio_list = []
|
| 333 |
+
count=0
|
| 334 |
+
while count<=3:
|
| 335 |
+
try:
|
| 336 |
+
documents=[item['content'] for item in documents_full]
|
| 337 |
+
document_summaries = [f"[DOCUMENT {i+1}]: {summary}{count}" for i, summary in enumerate(documents)]
|
| 338 |
+
documents_str='\n'.join(document_summaries)
|
| 339 |
+
formatted_prompt = system_instructions(
|
| 340 |
+
question_difficulty, topic,documents_str)
|
| 341 |
+
print(formatted_prompt)
|
| 342 |
+
pre_prompt = [
|
| 343 |
+
{"role": "system", "content": formatted_prompt}
|
| 344 |
+
]
|
| 345 |
+
response = client.text_generation(
|
| 346 |
+
formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False,
|
| 347 |
+
)
|
| 348 |
+
output_json = json.loads(f"{response}")
|
| 349 |
+
|
| 350 |
+
|
| 351 |
+
print(response)
|
| 352 |
+
print('output json', output_json)
|
| 353 |
+
|
| 354 |
+
global quiz_data
|
| 355 |
+
|
| 356 |
+
quiz_data = output_json
|
| 357 |
+
|
| 358 |
+
|
| 359 |
+
|
| 360 |
+
for question_num in range(1, 11):
|
| 361 |
+
question_key = f"Q{question_num}"
|
| 362 |
+
answer_key = f"A{question_num}"
|
| 363 |
+
|
| 364 |
+
question = quiz_data.get(question_key)
|
| 365 |
+
answer = quiz_data.get(quiz_data.get(answer_key))
|
| 366 |
+
|
| 367 |
+
if not question or not answer:
|
| 368 |
+
continue
|
| 369 |
+
|
| 370 |
+
choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
|
| 371 |
+
choice_list = []
|
| 372 |
+
for choice_key in choice_keys:
|
| 373 |
+
choice = quiz_data.get(choice_key, "Choice not found")
|
| 374 |
+
choice_list.append(f"{choice}")
|
| 375 |
+
|
| 376 |
+
radio = gr.Radio(choices=choice_list, label=question,
|
| 377 |
+
visible=True, interactive=True)
|
| 378 |
+
|
| 379 |
+
question_radio_list.append(radio)
|
| 380 |
+
if len(question_radio_list)==10:
|
| 381 |
+
break
|
| 382 |
+
else:
|
| 383 |
+
print('10 questions not generated . So trying again!')
|
| 384 |
+
count+=1
|
| 385 |
+
continue
|
| 386 |
+
except Exception as e:
|
| 387 |
+
count+=1
|
| 388 |
+
print(f"Exception occurred: {e}")
|
| 389 |
+
if count==3:
|
| 390 |
+
print('Retry exhausted')
|
| 391 |
+
gr.Warning('Sorry. Pls try with another topic !')
|
| 392 |
+
else:
|
| 393 |
+
print(f"Trying again..{count} time...please wait")
|
| 394 |
+
continue
|
| 395 |
+
|
| 396 |
+
print('Question radio list ' , question_radio_list)
|
| 397 |
+
|
| 398 |
+
return ['Quiz Generated!']+ question_radio_list
|
| 399 |
+
|
| 400 |
+
check_button = gr.Button("Check Score")
|
| 401 |
+
|
| 402 |
+
score_textbox = gr.Markdown()
|
| 403 |
+
|
| 404 |
+
@check_button.click(inputs=question_radios, outputs=score_textbox)
|
| 405 |
+
def compare_answers(*user_answers):
|
| 406 |
+
user_anwser_list = []
|
| 407 |
+
user_anwser_list = user_answers
|
| 408 |
+
|
| 409 |
+
answers_list = []
|
| 410 |
+
|
| 411 |
+
for question_num in range(1, 20):
|
| 412 |
+
answer_key = f"A{question_num}"
|
| 413 |
+
answer = quiz_data.get(quiz_data.get(answer_key))
|
| 414 |
+
if not answer:
|
| 415 |
+
break
|
| 416 |
+
answers_list.append(answer)
|
| 417 |
+
|
| 418 |
+
score = 0
|
| 419 |
+
|
| 420 |
+
for item in user_anwser_list:
|
| 421 |
+
if item in answers_list:
|
| 422 |
+
score += 1
|
| 423 |
+
if score>5:
|
| 424 |
+
message = f"### Good ! You got {score} over 10!"
|
| 425 |
+
elif score>7:
|
| 426 |
+
message = f"### Excellent ! You got {score} over 10!"
|
| 427 |
+
else:
|
| 428 |
+
message = f"### You got {score} over 10! Dont worry . You can prepare well and try better next time !"
|
| 429 |
+
|
| 430 |
+
return message
|
| 431 |
+
|
| 432 |
+
|
| 433 |
+
|
| 434 |
+
demo = gr.TabbedInterface([CHATBOT,QUIZBOT], ["AI ChatBot", "AI Nanban-Quizbot"])
|
| 435 |
+
|
| 436 |
+
demo.queue()
|
| 437 |
+
demo.launch(debug=True)
|
backend/query_llm.py
ADDED
|
@@ -0,0 +1,161 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
|
| 3 |
+
import openai
|
| 4 |
+
import gradio as gr
|
| 5 |
+
|
| 6 |
+
from os import getenv
|
| 7 |
+
from typing import Any, Dict, Generator, List
|
| 8 |
+
|
| 9 |
+
from huggingface_hub import InferenceClient
|
| 10 |
+
from transformers import AutoTokenizer
|
| 11 |
+
|
| 12 |
+
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
|
| 13 |
+
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
|
| 14 |
+
#tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
|
| 15 |
+
temperature = 0.5
|
| 16 |
+
top_p = 0.7
|
| 17 |
+
repetition_penalty = 1.2
|
| 18 |
+
|
| 19 |
+
OPENAI_KEY = getenv("OPENAI_API_KEY")
|
| 20 |
+
HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
|
| 21 |
+
|
| 22 |
+
# hf_client = InferenceClient(
|
| 23 |
+
# "mistralai/Mistral-7B-Instruct-v0.1",
|
| 24 |
+
# token=HF_TOKEN
|
| 25 |
+
# )
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
hf_client = InferenceClient(
|
| 29 |
+
"mistralai/Mixtral-8x7B-Instruct-v0.1",
|
| 30 |
+
token=HF_TOKEN
|
| 31 |
+
)
|
| 32 |
+
def format_prompt(message: str, api_kind: str):
|
| 33 |
+
"""
|
| 34 |
+
Formats the given message using a chat template.
|
| 35 |
+
|
| 36 |
+
Args:
|
| 37 |
+
message (str): The user message to be formatted.
|
| 38 |
+
|
| 39 |
+
Returns:
|
| 40 |
+
str: Formatted message after applying the chat template.
|
| 41 |
+
"""
|
| 42 |
+
|
| 43 |
+
# Create a list of message dictionaries with role and content
|
| 44 |
+
messages: List[Dict[str, Any]] = [{'role': 'user', 'content': message}]
|
| 45 |
+
|
| 46 |
+
if api_kind == "openai":
|
| 47 |
+
return messages
|
| 48 |
+
elif api_kind == "hf":
|
| 49 |
+
return tokenizer.apply_chat_template(messages, tokenize=False)
|
| 50 |
+
elif api_kind:
|
| 51 |
+
raise ValueError("API is not supported")
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
def generate_hf(prompt: str, history: str, temperature: float = 0.5, max_new_tokens: int = 4000,
|
| 55 |
+
top_p: float = 0.95, repetition_penalty: float = 1.0) -> Generator[str, None, str]:
|
| 56 |
+
"""
|
| 57 |
+
Generate a sequence of tokens based on a given prompt and history using Mistral client.
|
| 58 |
+
|
| 59 |
+
Args:
|
| 60 |
+
prompt (str): The initial prompt for the text generation.
|
| 61 |
+
history (str): Context or history for the text generation.
|
| 62 |
+
temperature (float, optional): The softmax temperature for sampling. Defaults to 0.9.
|
| 63 |
+
max_new_tokens (int, optional): Maximum number of tokens to be generated. Defaults to 256.
|
| 64 |
+
top_p (float, optional): Nucleus sampling probability. Defaults to 0.95.
|
| 65 |
+
repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
|
| 66 |
+
|
| 67 |
+
Returns:
|
| 68 |
+
Generator[str, None, str]: A generator yielding chunks of generated text.
|
| 69 |
+
Returns a final string if an error occurs.
|
| 70 |
+
"""
|
| 71 |
+
|
| 72 |
+
temperature = max(float(temperature), 1e-2) # Ensure temperature isn't too low
|
| 73 |
+
top_p = float(top_p)
|
| 74 |
+
|
| 75 |
+
generate_kwargs = {
|
| 76 |
+
'temperature': temperature,
|
| 77 |
+
'max_new_tokens': max_new_tokens,
|
| 78 |
+
'top_p': top_p,
|
| 79 |
+
'repetition_penalty': repetition_penalty,
|
| 80 |
+
'do_sample': True,
|
| 81 |
+
'seed': 42,
|
| 82 |
+
}
|
| 83 |
+
|
| 84 |
+
formatted_prompt = format_prompt(prompt, "hf")
|
| 85 |
+
|
| 86 |
+
try:
|
| 87 |
+
stream = hf_client.text_generation(formatted_prompt, **generate_kwargs,
|
| 88 |
+
stream=True, details=True, return_full_text=False)
|
| 89 |
+
output = ""
|
| 90 |
+
for response in stream:
|
| 91 |
+
output += response.token.text
|
| 92 |
+
yield output
|
| 93 |
+
|
| 94 |
+
except Exception as e:
|
| 95 |
+
if "Too Many Requests" in str(e):
|
| 96 |
+
print("ERROR: Too many requests on Mistral client")
|
| 97 |
+
gr.Warning("Unfortunately Mistral is unable to process")
|
| 98 |
+
return "Unfortunately, I am not able to process your request now."
|
| 99 |
+
elif "Authorization header is invalid" in str(e):
|
| 100 |
+
print("Authetification error:", str(e))
|
| 101 |
+
gr.Warning("Authentication error: HF token was either not provided or incorrect")
|
| 102 |
+
return "Authentication error"
|
| 103 |
+
else:
|
| 104 |
+
print("Unhandled Exception:", str(e))
|
| 105 |
+
gr.Warning("Unfortunately Mistral is unable to process")
|
| 106 |
+
return "I do not know what happened, but I couldn't understand you."
|
| 107 |
+
|
| 108 |
+
|
| 109 |
+
def generate_openai(prompt: str, history: str, temperature: float = 0.9, max_new_tokens: int = 256,
|
| 110 |
+
top_p: float = 0.95, repetition_penalty: float = 1.0) -> Generator[str, None, str]:
|
| 111 |
+
"""
|
| 112 |
+
Generate a sequence of tokens based on a given prompt and history using Mistral client.
|
| 113 |
+
|
| 114 |
+
Args:
|
| 115 |
+
prompt (str): The initial prompt for the text generation.
|
| 116 |
+
history (str): Context or history for the text generation.
|
| 117 |
+
temperature (float, optional): The softmax temperature for sampling. Defaults to 0.9.
|
| 118 |
+
max_new_tokens (int, optional): Maximum number of tokens to be generated. Defaults to 256.
|
| 119 |
+
top_p (float, optional): Nucleus sampling probability. Defaults to 0.95.
|
| 120 |
+
repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
|
| 121 |
+
|
| 122 |
+
Returns:
|
| 123 |
+
Generator[str, None, str]: A generator yielding chunks of generated text.
|
| 124 |
+
Returns a final string if an error occurs.
|
| 125 |
+
"""
|
| 126 |
+
|
| 127 |
+
temperature = max(float(temperature), 1e-2) # Ensure temperature isn't too low
|
| 128 |
+
top_p = float(top_p)
|
| 129 |
+
|
| 130 |
+
generate_kwargs = {
|
| 131 |
+
'temperature': temperature,
|
| 132 |
+
'max_tokens': max_new_tokens,
|
| 133 |
+
'top_p': top_p,
|
| 134 |
+
'frequency_penalty': max(-2., min(repetition_penalty, 2.)),
|
| 135 |
+
}
|
| 136 |
+
|
| 137 |
+
formatted_prompt = format_prompt(prompt, "openai")
|
| 138 |
+
|
| 139 |
+
try:
|
| 140 |
+
stream = openai.ChatCompletion.create(model="gpt-3.5-turbo-0301",
|
| 141 |
+
messages=formatted_prompt,
|
| 142 |
+
**generate_kwargs,
|
| 143 |
+
stream=True)
|
| 144 |
+
output = ""
|
| 145 |
+
for chunk in stream:
|
| 146 |
+
output += chunk.choices[0].delta.get("content", "")
|
| 147 |
+
yield output
|
| 148 |
+
|
| 149 |
+
except Exception as e:
|
| 150 |
+
if "Too Many Requests" in str(e):
|
| 151 |
+
print("ERROR: Too many requests on OpenAI client")
|
| 152 |
+
gr.Warning("Unfortunately OpenAI is unable to process")
|
| 153 |
+
return "Unfortunately, I am not able to process your request now."
|
| 154 |
+
elif "You didn't provide an API key" in str(e):
|
| 155 |
+
print("Authetification error:", str(e))
|
| 156 |
+
gr.Warning("Authentication error: OpenAI key was either not provided or incorrect")
|
| 157 |
+
return "Authentication error"
|
| 158 |
+
else:
|
| 159 |
+
print("Unhandled Exception:", str(e))
|
| 160 |
+
gr.Warning("Unfortunately OpenAI is unable to process")
|
| 161 |
+
return "I do not know what happened, but I couldn't understand you."
|
backend/semantic_search.py
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
import logging
|
| 3 |
+
import lancedb
|
| 4 |
+
import os
|
| 5 |
+
from pathlib import Path
|
| 6 |
+
from sentence_transformers import SentenceTransformer
|
| 7 |
+
#from FlagEmbedding import LLMEmbedder, FlagReranker # Al document present here https://github.com/FlagOpen/FlagEmbedding/tree/master
|
| 8 |
+
#EMB_MODEL_NAME = "thenlper/gte-base"
|
| 9 |
+
EMB_MODEL_NAME = 'BAAI/llm-embedder'
|
| 10 |
+
task = "qa" # Encode for a specific task (qa, icl, chat, lrlm, tool, convsearch)
|
| 11 |
+
#EMB_MODEL_NAME = LLMEmbedder('BAAI/llm-embedder', use_fp16=False) # Load model (automatically use GPUs)
|
| 12 |
+
|
| 13 |
+
#reranker_model = FlagReranker('BAAI/bge-reranker-base', use_fp16=True) # use_fp16 speeds up computation with a slight performance degradation
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
#EMB_MODEL_NAME = "thenlper/gte-base"
|
| 17 |
+
#DB_TABLE_NAME = "Huggingface_docs"
|
| 18 |
+
DB_TABLE_NAME = "Cus_manual_"
|
| 19 |
+
# Setting up the logging
|
| 20 |
+
logging.basicConfig(level=logging.INFO)
|
| 21 |
+
logger = logging.getLogger(__name__)
|
| 22 |
+
retriever = SentenceTransformer(EMB_MODEL_NAME)
|
| 23 |
+
|
| 24 |
+
# db
|
| 25 |
+
db_uri = os.path.join(Path(__file__).parents[1], ".lancedb")
|
| 26 |
+
print(f'DB URL is {db_uri}')
|
| 27 |
+
db = lancedb.connect(db_uri)
|
| 28 |
+
table = db.open_table(DB_TABLE_NAME)
|
lancedb/Cus_manual_.lance/_latest.manifest
ADDED
|
Binary file (251 Bytes). View file
|
|
|
lancedb/Cus_manual_.lance/_transactions/0-68be5234-afdd-46a3-8d8f-f15d3ddf8d9f.txn
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
$68be5234-afdd-46a3-8d8f-f15d3ddf8d9f�U text ���������*string084vector ���������*fixed_size_list:float:76808
|
lancedb/Cus_manual_.lance/_transactions/1-a6eb2f8c-2ce4-402b-b494-e584b2cbd5df.txn
ADDED
|
Binary file (98 Bytes). View file
|
|
|
lancedb/Cus_manual_.lance/_versions/1.manifest
ADDED
|
Binary file (196 Bytes). View file
|
|
|
lancedb/Cus_manual_.lance/_versions/2.manifest
ADDED
|
Binary file (251 Bytes). View file
|
|
|
lancedb/Cus_manual_.lance/data/d98dfa8d-3da9-429d-9225-799f5c713440.lance
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
logo.png
ADDED
|
requirements.txt
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
sentence-transformers==2.6.1
|
| 3 |
+
firebase_admin
|
| 4 |
+
huggingface-hub==0.19.3
|
| 5 |
+
spaces
|
| 6 |
+
ragatouille
|
| 7 |
+
transformers
|
| 8 |
+
lancedb
|
| 9 |
+
gradio==4.27.0
|
templates/template.j2
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Instructions: You are assistant to School Children and Teachers .You have to answer their questions and tasks asked by them.Use the following unique CBSE Class chapters data in the Context section to answer the Query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.remember the chapter number and name of the chapter Chapter 1 Chemical Reactions a.nd Equations Chapter 2- Acids, Bases and Salts.Chapter 3 Metals and Non-metals. Chapter 4 Carbon and Its Compounds. Chapter 5 Life Processes. Chapter 6 - Control and Coordination .Chapter 7 - How do Organisms Reproduce? .Chapter 8 Heredity .Chapter - 9 Light Reflection and Refraction .Chapter 10- Human Eye and Colourful World .Chapter 11 - Electricity .Chapter 12 - Magnetic Effects of Electric Current .Chapter 13 - Our Environment
|
| 2 |
+
Context:
|
| 3 |
+
{% for doc in documents %}
|
| 4 |
+
---
|
| 5 |
+
{{ doc }}
|
| 6 |
+
{% endfor %}
|
| 7 |
+
---
|
| 8 |
+
Query: {{ query }}
|
templates/template_html.j2
ADDED
|
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
<!DOCTYPE html>
|
| 2 |
+
<html lang="en">
|
| 3 |
+
<head>
|
| 4 |
+
<meta charset="UTF-8">
|
| 5 |
+
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
| 6 |
+
<title>Information Page</title>
|
| 7 |
+
<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap">
|
| 8 |
+
<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&display=swap">
|
| 9 |
+
<style>
|
| 10 |
+
* {
|
| 11 |
+
font-family: "Source Sans Pro";
|
| 12 |
+
}
|
| 13 |
+
|
| 14 |
+
.instructions > * {
|
| 15 |
+
color: #111 !important;
|
| 16 |
+
}
|
| 17 |
+
|
| 18 |
+
details.doc-box * {
|
| 19 |
+
color: #111 !important;
|
| 20 |
+
}
|
| 21 |
+
|
| 22 |
+
.dark {
|
| 23 |
+
background: #111;
|
| 24 |
+
color: white;
|
| 25 |
+
}
|
| 26 |
+
|
| 27 |
+
.doc-box {
|
| 28 |
+
padding: 10px;
|
| 29 |
+
margin-top: 10px;
|
| 30 |
+
background-color: #baecc2;
|
| 31 |
+
border-radius: 6px;
|
| 32 |
+
color: #111 !important;
|
| 33 |
+
max-width: 700px;
|
| 34 |
+
box-shadow: rgba(0, 0, 0, 0.2) 0px 1px 2px 0px;
|
| 35 |
+
}
|
| 36 |
+
|
| 37 |
+
.doc-full {
|
| 38 |
+
margin: 10px 14px;
|
| 39 |
+
line-height: 1.6rem;
|
| 40 |
+
}
|
| 41 |
+
|
| 42 |
+
.instructions {
|
| 43 |
+
color: #111 !important;
|
| 44 |
+
background: #b7bdfd;
|
| 45 |
+
display: block;
|
| 46 |
+
border-radius: 6px;
|
| 47 |
+
padding: 6px 10px;
|
| 48 |
+
line-height: 1.6rem;
|
| 49 |
+
max-width: 700px;
|
| 50 |
+
box-shadow: rgba(0, 0, 0, 0.2) 0px 1px 2px 0px;
|
| 51 |
+
}
|
| 52 |
+
|
| 53 |
+
.query {
|
| 54 |
+
color: #111 !important;
|
| 55 |
+
background: #ffbcbc;
|
| 56 |
+
display: block;
|
| 57 |
+
border-radius: 6px;
|
| 58 |
+
padding: 6px 10px;
|
| 59 |
+
line-height: 1.6rem;
|
| 60 |
+
max-width: 700px;
|
| 61 |
+
box-shadow: rgba(0, 0, 0, 0.2) 0px 1px 2px 0px;
|
| 62 |
+
}
|
| 63 |
+
</style>
|
| 64 |
+
</head>
|
| 65 |
+
<body>
|
| 66 |
+
<div class="prose svelte-1ybaih5" id="component-6">
|
| 67 |
+
<h2>Prompt</h2>
|
| 68 |
+
Below is the prompt that is given to the model. <hr>
|
| 69 |
+
<h2>Instructions</h2>
|
| 70 |
+
<span class="instructions">You are assistant to School Children and Teachers .You have to answer their questions and tasks asked by them.Use the following unique CBSE Class chapters data in the Context section to answer the Query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.<br>If you don't know the answer, just say that you don't know, <span style="font-weight: bold;">don't try to make up an answer.</span></span><br>
|
| 71 |
+
<h2>Context</h2>
|
| 72 |
+
{% for doc in documents %}
|
| 73 |
+
<details class="doc-box">
|
| 74 |
+
<summary>
|
| 75 |
+
<b>Doc {{ loop.index }}:</b> <span class="doc-short">{{ doc[:100] }}...</span>
|
| 76 |
+
</summary>
|
| 77 |
+
<div class="doc-full">{{ doc }}</div>
|
| 78 |
+
</details>
|
| 79 |
+
{% endfor %}
|
| 80 |
+
|
| 81 |
+
<h2>Query</h2>
|
| 82 |
+
<span class="query">{{ query }}</span>
|
| 83 |
+
</div>
|
| 84 |
+
|
| 85 |
+
<script>
|
| 86 |
+
document.addEventListener("DOMContentLoaded", function() {
|
| 87 |
+
const detailsElements = document.querySelectorAll('.doc-box');
|
| 88 |
+
|
| 89 |
+
detailsElements.forEach(detail => {
|
| 90 |
+
detail.addEventListener('toggle', function() {
|
| 91 |
+
const docShort = this.querySelector('.doc-short');
|
| 92 |
+
if (this.open) {
|
| 93 |
+
docShort.style.display = 'none';
|
| 94 |
+
} else {
|
| 95 |
+
docShort.style.display = 'inline';
|
| 96 |
+
}
|
| 97 |
+
});
|
| 98 |
+
});
|
| 99 |
+
});
|
| 100 |
+
</script>
|
| 101 |
+
</body>
|
| 102 |
+
</html>
|