Update app.py

app.py

@@ -1,117 +0,0 @@
-from langchain_core.prompts import PromptTemplate
-import os 
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain_community.llms.ctransformers import CTransformers
-from langchain.chains.retrieval_qa.base import RetrievalQA
-import streamlit as st
-
-DB_FAISS_PATH = 'vectorstores/'
-
-custom_prompt_template = '''use the following pieces of information to answer the user's questions.
-If you don't know the answer, please just say that don't know the answer, don't try to make up an answer.
-Context : {context}
-Question : {question}
-only return the helpful answer below and nothing else.
-'''
-
-# custom_prompt_template = '''
-# <|im_start|>system
-# use the following pieces of information to answer the user's questions.
-# If you don't know the answer, please just say that don't know the answer, don't try to make up an answer.
-# Context : {context}
-# Question : {question}
-# only return the helpful answer below and nothing else.
-# '''
-
-
-
-def set_custom_prompt():
-    """
-    Prompt template for QA retrieval for vector stores
-    """
-    prompt = PromptTemplate(template=custom_prompt_template,
-                            input_variables=['context', 'question'])
-    return prompt
-
-def load_llm():
-    llm = CTransformers(
-        model='epfl-llm/meditron-7b',
-        model_type='llma',
-        max_new_token=512,
-        temperature=0.5
-    )
-    return llm
-
-def load_embeddings():
-    embeddings = HuggingFaceBgeEmbeddings(model_name='NeuML/pubmedbert-base-embeddings',
-                                          model_kwargs={'device': 'cpu'})
-    return embeddings
-
-def load_faiss_index(embeddings):
-    db = FAISS.load_local(DB_FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
-    return db
-
-def retrieval_qa_chain(llm, prompt, db):
-    qa_chain = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type='stuff',
-        retriever=db.as_retriever(search_kwargs={'k': 2}),
-        return_source_documents=True,
-        chain_type_kwargs={'prompt': prompt}
-    )
-    return qa_chain
-
-def qa_bot():
-    embeddings = load_embeddings()
-    db = load_faiss_index(embeddings)
-    llm = load_llm()
-    qa_prompt = set_custom_prompt()
-    qa = retrieval_qa_chain(llm, qa_prompt, db)
-    return qa
-
-def final_result(query):
-    qa_result = qa_bot()
-    response = qa_result({'query': query})
-    return response
-
-import streamlit as st
-
-# Initialize the bot
-bot = qa_bot()
-
-# Streamlit webpage title
-st.title('Medical Chatbot')
-
-# User input
-user_query = st.text_input("Please enter your question:")
-
-# Button to get answer
-if st.button('Get Answer'):
-    if user_query:
-        # Call the function from your chatbot script
-        response = final_result(user_query)
-        if response:
-            # Displaying the response
-            st.write("### Answer")
-            st.write(response['result'])
-
-            # Displaying source document details if available
-            if 'source_documents' in response:
-                st.write("### Source Document Information")
-                for doc in response['source_documents']:
-                    # Retrieve and format page content by replacing '\n' with new line
-                    formatted_content = doc.page_content.replace("\\n", "\n")
-                    st.write("#### Document Content")
-                    st.text_area(label="Page Content", value=formatted_content, height=300)
-
-                    # Retrieve source and page from metadata
-                    source = doc.metadata.get('source', 'Unknown')
-                    page = doc.metadata.get('page', 'Unknown')
-                    st.write(f"Source: {source}")
-                    st.write(f"Page Number: {page}")
-                    
-        else:
-            st.write("Sorry, I couldn't find an answer to your question.")
-    else:
-        st.write("Please enter a question to get an answer.")