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import streamlit as st
from src.helper import download_hugging_face_embeddings
from langchain.vectorstores import FAISS
from langchain.schema import Document
from langchain.llms import CTransformers
from langchain.chains import RetrievalQA
from dotenv import load_dotenv
import os
app = Flask(__name__)
load_dotenv()
# Download embeddings model
embeddings = download_hugging_face_embeddings()
# Create Document objects with dummy texts and embeddings
documents = [Document(page_content="dummy", embedding=embedding) for embedding in embeddings]
# Initialize FAISS vector store with documents
vector_store = FAISS.from_documents(documents, embeddings)
# Initialize CTransformers model (LLAMA)
llm = CTransformers(model="E:\\project\\Medical-Chatbot\\llama-2-7b-chat.ggmlv3.q4_0.bin", model_type="llama", config={'max_new_tokens': 512, 'temperature': 0.8})
# Initialize RetrievalQA chain
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vector_store.as_retriever(search_kwargs={'k': 2}),
return_source_documents=True
)
@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)
result = qa({"query": input})
print("Response : ", result["result"])
return str(result["result"])
if __name__ == '__main__':
app.run(host="0.0.0.0", port=8080, debug=True)
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