Delete app.py

app.py

@@ -1,121 +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 import RetrievalQA
-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 uo 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 = 'TheBloke/Llama-2-7B-Chat-GGML',
-        #model = AutoModel.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML"),
-        model = 'MaziyarPanahi/BioMistral-7B-GGUF',
-        model_type = 'mistral',
-        max_new_token = 512,
-        temperature = 0.5
-    )
-    return llm
-
-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 = HuggingFaceBgeEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2',
-    #                                      model_kwargs = {'device':'cpu'})
-    embeddings = HuggingFaceBgeEmbeddings(model_name = 'NeuML/pubmedbert-base-embeddings',
-                                         model_kwargs =  {'device':'cpu'} )
-    
-    
-    db = FAISS.load_local(DB_FAISS_PATH, embeddings,allow_dangerous_deserialization=True)
-    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()
-
-# def process_query(query):
-#     # Here you would include the logic to process the query and return a response
-#     response, sources = bot.answer_query(query)  # Modify this according to your bot implementation
-#     if sources:
-#         response += f"\nSources: {', '.join(sources)}"
-#     else:
-#         response += "\nNo Sources Found"
-#     return response
-
-
-# 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['source']
-                    page = doc.metadata['page']
-                    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.")