adding files

Spelling_correction/model/config.json

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+{
+  "_name_or_path": "t5-small",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 6,
+  "num_heads": 8,
+  "num_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

Spelling_correction/model/pytorch_model.bin

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Spelling_correction/tokenizer/special_tokens_map.json

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+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

Spelling_correction/tokenizer/spiece.model

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Spelling_correction/tokenizer/tokenizer_config.json

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+{
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+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "model_max_length": 512,
+  "name_or_path": "t5-small",
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
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+  "tokenizer_class": "T5Tokenizer",
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+}

api.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from utils import model_prediction
+
+app = FastAPI()
+
+class TextRequest(BaseModel):
+    text: str
+
+class TextResponse(BaseModel):
+    original_text: str
+    corrected_text: str
+
+@app.post("/predict", response_model=TextResponse)
+def predict(request: TextRequest):
+    corrected_text = model_prediction(request.text)
+    return TextResponse(original_text=request.text, corrected_text=corrected_text)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

app.py

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+import streamlit as st
+from utils import model_prediction
+import requests
+
+# set the page config
+st.set_page_config(page_title="Sentence Correction", page_icon="📝", layout="centered")
+
+# set the title
+st.title("Sentence Correction")
+
+user_input=st.text_area("Enter text: ")
+
+if st.button("Remove Error"):
+    with st.status("Hitting the api please wait...."):
+        try:
+            response=requests.post("http://localhost:8000/predict", json={"text": user_input})
+            if response.status_code == 200:
+                result = response.json()
+                st.error(f"Original Text: {result['original_text']}")
+                st.success(f"Model Prediction: {result['corrected_text']}")
+                # st.json(result)
+            else:
+                st.error("Error in fetching prediction from the API.")
+        except Exception as e:
+            st.write(f"There should be some issue in the api serving {e}")
+
+
+

requirements.txt

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+transformers
+numpy
+pandas
+streamlit
+tensorflow
+torch==2.3.1

utils.py

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+# Load the model and tokenizer
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import logging
+logging.basicConfig(level=logging.INFO)
+
+print(torch.__version__)
+def load_tokenizer():
+    try:
+        tokenizer = T5Tokenizer.from_pretrained('Spelling_correction/tokenizer')
+        return tokenizer
+    except Exception as e:
+        f"some error occur {e}"
+        return None
+
+def load_model():
+    try:
+        model = T5ForConditionalGeneration.from_pretrained('Spelling_correction/model')
+
+        return model
+    except Exception as e:
+        f"Some error occur {e}"
+        return None
+def model_prediction(text):
+    tokenizer=load_tokenizer()
+    
+    input_ids = tokenizer.encode(text, return_tensors='pt') # Move input_ids to the GPU
+    
+    model=load_model()
+    outputs = model.generate(input_ids, max_length=128)
+    corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return corrected_text