Create app.py

app.py

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+
+
+
+import time
+import dotenv
+import streamlit as st
+from langchain_core.messages import HumanMessage, AIMessage
+from dotenv import load_dotenv
+
+
+from langchain_core.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Pinecone
+
+
+import pinecone
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.prompts import PromptTemplate
+from langchain_community.llms import CTransformers
+
+
+
+load_dotenv()
+st.set_page_config(page_title= "Medical chatbot", page_icon=":bot:")
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+PINECONE_API_KEY = "1bae0d8e-019e-4e87-8080-ecf523e5f25f"
+def get_response(user_query):
+    # Initilize the prompt
+    # create prompt template, integrate chatHistory component as well
+    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.
+
+        Context: {context}
+        Question: {question}
+
+        Only return the helpful answer below nothing else.
+        Helpful Answer: 
+        """
+    
+    PROMPT = PromptTemplate(template = prompt_template, input_variables=["context", "question"])
+    chain_type_kwargs = {"prompt":PROMPT}
+
+    llm = CTransformers(model="TheBloke/Llama-2-7B-Chat-GGML", model_type="llama", config={'max_new_tokens': 1024, 'temperature': 1})
+    
+    index_name = "medical-chatbot"
+    index=pinecone.Index(api_key=PINECONE_API_KEY, host="https://medical-chatbot-pv4ded8.svc.aped-4627-b74a.pinecone.io")
+
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    # Create Pinecone retriever
+    vector_store = Pinecone(index, embeddings, text_key="text")
+
+    
+    qa = RetrievalQA.from_chain_type(llm, chain_type="stuff",retriever = vector_store.as_retriever(search_kwargs={"k": 2}), chain_type_kwargs=chain_type_kwargs)
+    answer = qa.invoke({"query":user_query, "context": st.session_state.chat_history})
+
+    return answer
+    # answer = vector_store.similarity_search(user_query, k=3)
+    # return answer.stream().get("answer")
+
+# Function to simulate typing effect
+def type_effect(text):
+    for char in text:
+        st.write(char)
+        time.sleep(0.05)
+    st.write("")
+
+    
+st.title("Medical chatbot")
+
+st.write("Welcome to the medical chatbot. Please enter your symptoms below and I will try to help you.")
+
+if "chat_history" in st.session_state:
+    for message in st.session_state.chat_history:
+        if "user" in message:
+            with st.chat_message("Human"):
+                st.markdown(message["user"])
+        elif "bot" in message:
+            with st.chat_message("AI"):
+                st.markdown(message["bot"])
+
+user_query = st.chat_input("Enter your symptoms here")
+if user_query is not None and user_query != "":
+
+
+    with st.chat_message("Human"):
+        st.markdown(user_query)
+        st.session_state.chat_history.append({"user": user_query})
+
+    with st.chat_message("AI"):
+        # =""
+        # for message in st.session_state.chat_history:
+        #     if "user" in message:
+        #        += f"User: {message['user']}\n"
+        #     elif "bot" in message:
+        #        += f"Bot: {message['bot']}\n"
+
+        ai_response = get_response(user_query)
+        # st.write(type(ai_response))
+        result = ai_response["result"]
+        # type_effect(result)
+        st.markdown(result)
+
+        # Get the response from backend and present it here
+        st.session_state.chat_history.append({"bot": result})
+
+
+
+# import os
+# import time
+# import dotenv
+# import streamlit as st
+# from dotenv import load_dotenv
+
+# from langchain import PromptTemplate
+# from langchain.chains import RetrievalQA
+# from langchain.embeddings import HuggingFaceEmbeddings
+# from langchain.vectorstores import Pinecone
+
+# import pinecone
+# from langchain.llms import CTransformers
+
+# # Load environment variables
+# load_dotenv()
+
+# # Initialize Streamlit page config
+# st.set_page_config(page_title="Medical Chatbot", page_icon=":bot:")
+
+# # Initialize chat history in session state
+# if "chat_history" not in st.session_state:
+#     st.session_state.chat_history = []
+
+# PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+# HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
+
+# # Cache models and vector store initialization
+# embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+# @st.cache_resource
+# def initialize_models():
+#     # Load language model
+
+#     llm = CTransformers(model="model/llama-2-7b-chat.ggmlv3.q4_0.bin", model_type="llama", config={'max_new_tokens': 1024, 'temperature': 1})
+
+
+#     # Initialize Pinecone index
+#     index = pinecone.Index(api_key=PINECONE_API_KEY, host="https://medical-chatbot-pv4ded8.svc.aped-4627-b74a.pinecone.io")
+
+#     # Initialize embeddings
+
+#     # Create Pinecone retriever
+#     vector_store = Pinecone(index, embeddings, text_key="text")
+
+#     return llm, vector_store
+
+# llm, vector_store = initialize_models()
+
+# # Define prompt template
+# prompt_template = """
+#     Use the following pieces of information to answer the user's question.
+#     If you don't know the answer, just say that I don't know, don't try to make up an answer.
+
+#     Context: {context}
+#     Question: {question}
+
+#     Only return the helpful answer below nothing else.
+#     Helpful Answer: 
+#     """
+# PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+
+# # Cache QA chain initialization
+# @st.cache_resource
+# def _initialize_qa(_llm, _vector_store):
+#     return RetrievalQA.from_chain_type(
+#         _llm,
+#         chain_type="stuff",
+#         retriever=_vector_store.as_retriever(search_kwargs={"k": 2}),
+#         chain_type_kwargs={"prompt": PROMPT}
+#     )
+
+# qa = _initialize_qa(llm, vector_store)
+
+# def get_response(user_query):
+#     # chat_context = "\n".join([f"User: {msg['user']}" if 'user' in msg else f"Bot: {msg['bot']}" for msg in st.session_state.chat_history])
+#     answer = qa.invoke({"query": user_query, "context": st.session_state.chat_history})
+#     return answer
+
+# # Function to simulate typing effect
+# # def type_effect(text):
+# #     for char in text:
+# #         st.write(char, end="")
+# #         time.sleep(0.05)
+# #     st.write("")
+
+# # Streamlit UI
+# st.title("Medical Chatbot")
+# st.write("Welcome to the medical chatbot. Please enter your symptoms below and I will try to help you.")
+
+# # Display chat history
+# for message in st.session_state.chat_history:
+#     if "user" in message:
+#         with st.chat_message("Human"):
+#             st.markdown(message["user"])
+#     elif "bot" in message:
+#         with st.chat_message("AI"):
+#             st.markdown(message["bot"])
+
+# # Chat input and response handling
+# user_query = st.chat_input("Enter your symptoms here")
+# if user_query:
+#     with st.chat_message("Human"):
+#         st.markdown(user_query)
+#         st.session_state.chat_history.append({"user": user_query})
+
+#     with st.chat_message("AI"):
+#         ai_response = get_response(user_query)
+#         result = ai_response["result"]
+#         st.markdown(result)
+#         st.session_state.chat_history.append({"bot": result})