Update app.py

app.py

@@ -1 +1,78 @@
-
              chain = ConversationalRetrievalChain.from_llm(llm=llm,retriever=retriever)
              print("LLM or Vector Database not initialized")
    history_langchain_format = []
    prompt = PromptTemplate(template=prompt_template,
    input_variables=["chat_history", 'message'])
    response = chain({"question": message, "chat_history": chat_history})
    
    answer = response['answer']
    
    chat_history.append((message, answer))
    
    temp = []
    for input_question, bot_answer in history:
         temp.append(input_question)
         temp.append(bot_answer)
         history_langchain_format.append(temp)
    temp.clear()
    temp.append(message)
    temp.append(answer)
    history_langchain_format.append(temp)
    
    return answer
+
+from langchain import PromptTemplate
+from langchain_community.llms import LlamaCpp
+from langchain.chains import RetrievalQA
+from langchain.chains import ConversationalRetrievalChain
+from langchain.prompts import SystemMessagePromptTemplate
+from langchain_community.embeddings import SentenceTransformerEmbeddings
+from fastapi import FastAPI, Request, Form, Response
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
+from fastapi.staticfiles import StaticFiles
+from fastapi.encoders import jsonable_encoder
+from qdrant_client import QdrantClient
+from langchain_community.vectorstores import Qdrant
+import os
+import json
+import gradio as gr
+import sys
+
+#sys.path.insert(0, <envs\myenv\lib\site-packages>).
+
+local_llm = "BioMistral-7B.Q4_K_M.gguf"
+llm = LlamaCpp(model_path=
+local_llm,temperature=0.3,max_tokens=2048,top_p=1,n_ctx= 2048)
+
+prompt_template = """Use the following pieces of information to answer the user's question.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+Chat History: {chat_history}
+Question: {question}
+
+Only return the helpful answer. Answer must be detailed and well explained.
+Helpful answer:
+"""
+
+embeddings = SentenceTransformerEmbeddings(model_name="NeuML/pubmedbert-base-embeddings")
+
+url = "http://localhost:6333"
+client = QdrantClient(url=url, prefer_grpc=False)
+db = Qdrant(client=client, embeddings=embeddings, collection_name="vector_db")
+
+retriever = db.as_retriever(search_kwargs={"k":1})
+
+chat_history = []
+
+# Create the custom chain
+if llm is not None and db is not None:
+              chain = ConversationalRetrievalChain.from_llm(llm=llm,retriever=retriever)
+else:
+              print("LLM or Vector Database not initialized")
+
+def predict(message, history):
+    history_langchain_format = []
+
+    prompt = PromptTemplate(template=prompt_template,
+    input_variables=["chat_history", 'message'])
+
+    response = chain({"question": message, "chat_history": chat_history})
+    
+    answer = response['answer']
+    
+    chat_history.append((message, answer))
+    
+    temp = []
+    for input_question, bot_answer in history:
+         temp.append(input_question)
+         temp.append(bot_answer)
+         history_langchain_format.append(temp)
+    temp.clear()
+    temp.append(message)
+    temp.append(answer)
+    history_langchain_format.append(temp)
+    
+    return answer
+
+
+gr.ChatInterface(predict).launch(share=True,enable_queue=True)
+