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Configuration error
Configuration error
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print('bdldjfld')
from flask import Flask, render_template, jsonify, request
from src.helper import download_embeddings
from langchain_pinecone import PineconeVectorStore
from langchain_openai import ChatOpenAI
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.schema import Document
from dotenv import load_dotenv
from src.prompt import *
import os
from transformers import pipeline
from langchain_community.llms import HuggingFacePipeline
# Cleaning function for model output
import re
def clean_text(text):
text = re.sub(r'-\n', '', text)
text = text.replace('\n', ' ')
text = re.sub(r' +', ' ', text)
text = re.sub(r'/C\d+', '', text)
# Remove lines with 'Reproduced by permission' or 'GALE ENCYCLOPEDIA OF MEDICINE'
text = re.sub(r'Reproduced by permission[^\.\n]*[\.]?', '', text, flags=re.IGNORECASE)
text = re.sub(r'GALE ENCYCLOPEDIA OF MEDICINE[^\.\n]*[\.]?', '', text, flags=re.IGNORECASE)
text = re.sub(r'Researchers,? Inc[^\.\n]*[\.]?', '', text, flags=re.IGNORECASE)
return text.strip()
app = Flask(__name__)
# Add a greeting document to the Pinecone index
load_dotenv()
Pinecone_API_KEY = os.getenv("Pinecone_API_KEY")
os.environ["Pinecone_API_KEY"] = Pinecone_API_KEY
if not Pinecone_API_KEY:
raise ValueError("Missing Pinecone API key. Set it in Hugging Face secrets.")
embeddings = download_embeddings()
index_name = "doctor-ai"
docsearch=PineconeVectorStore.from_existing_index(
index_name=index_name,
embedding=embeddings
)
retriever= docsearch.as_retriever(search_type="similarity",search_kwargs={"k": 3})
generator = pipeline( "text2text-generation", model="google/flan-t5-large", device=-1)
chat_model = HuggingFacePipeline(pipeline=generator)
#prompt= ChatPromptTemplate.from_messages(
# [
# ("system", system_prompt),
# ("human", "{input}")
# ]
#)
prompt = ChatPromptTemplate.from_template(system_prompt)
question_answer_chain = create_stuff_documents_chain(chat_model, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
#question_answer_chain = create_stuff_documents_chain(chat_model, prompt)
#rag_chain = create_retrieval_chain(retriever, question_answer_chain)
@app.route('/')
def index():
return render_template('chat.html')
@app.route('/get', methods=["GET","POST"])
def chat():
msg=request.form["msg"]
input=msg
print(input)
response = rag_chain.invoke({"input": input})
cleaned_answer = clean_text(response["answer"])
print("Response:", cleaned_answer)
return str(cleaned_answer)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860, debug=True)
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