Spaces:
Sleeping
Sleeping
File size: 4,027 Bytes
eda7190 219ce27 4913dc3 219ce27 4913dc3 219ce27 eda7190 c9bcfeb eda7190 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
import gradio as gr
from huggingface_hub import InferenceClient
import os
import openai
import pandas as pd
import faiss
import pickle
from sentence_transformers import SentenceTransformer
embedding_model = SentenceTransformer('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True)
openai.api_key = os.getenv("OPENAI_API_KEY")
db_index = faiss.read_index("db_index.faiss")
df = pd.read_csv('cleaned_data.csv')
with open('metadata_info.pkl', 'rb') as file:
metadata_info = pickle.load(file)
def search(query):
cleaned_query = query
query_embedding = embedding_model.encode(cleaned_query).reshape(1, -1).astype('float32')
D, I = db_index.search(query_embedding, k=10)
results = []
for idx in I[0]:
if idx < 3327:
doc_index = idx
results.append({
'type': 'metadata',
'title': df.iloc[doc_index]['title'],
'author': df.iloc[doc_index]['author'],
'publish_date': df.iloc[doc_index]['publish_date'],
'full_text': df.iloc[doc_index]['full_text'],
'source': df.iloc[doc_index]['url']
})
else:
chunk_index = idx - 3327
metadata = metadata_info[chunk_index]
doc_index = metadata['index']
chunk_text = metadata['chunk']
results.append({
'type': 'content',
'title': df.iloc[doc_index]['title'],
'author': df.iloc[doc_index]['author'],
'publish_date': df.iloc[doc_index]['publish_date'],
'content': chunk_text,
'source': df.iloc[doc_index]['url']
})
return results
def generate_answer(query):
prompt = f"""
Based on the following query from a user, please generate a detailed answer based on the context
focusing on which is the best based on the query. You should respond as a news and politician expert agent conversing with the
user in a nice cordial way. If the query question is not in the context say I don't know, and always provide the url as the source of the information.
Remove the special characters and (/n ), and make the output clean and concise.
###########
query:
"{query}"
########
context:"
"{search(query)}"
#####
Return in Markdown format with each hotel highlighted.
"""
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
max_tokens=1500,
n=1,
stop=None,
temperature=0.2, #higher temperature means more creative or more hallucination
messages = messages
)
# Extract the generated response from the API response
generated_text = response.choices[0].message['content'].strip()
return generated_text
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co./docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
response = generate_answer(message)
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |