changed chatmultipledocuments

chatmultipledocuments/.env

@@ -0,0 +1 @@
+GOOGLE_API_KEY=

chatmultipledocuments/chatpdf1.py

@@ -10,39 +10,34 @@
 from langchain.prompts import PromptTemplate
 from dotenv import load_dotenv
 
+# Load environment variables and configure Google API
 load_dotenv()
 os.getenv("GOOGLE_API_KEY")
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-
-
-
-
-
 def get_pdf_text(pdf_docs):
-    text=""
+    """Extract text from uploaded PDF documents"""
+    text = ""
     for pdf in pdf_docs:
-        pdf_reader= PdfReader(pdf)
+        pdf_reader = PdfReader(pdf)
         for page in pdf_reader.pages:
-            text+= page.extract_text()
-    return  text
-
-
+            text += page.extract_text()
+    return text
 
 def get_text_chunks(text):
+    """Split text into manageable chunks"""
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
     chunks = text_splitter.split_text(text)
     return chunks
 
-
 def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    """Create and save FAISS vector store"""
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
     vector_store.save_local("faiss_index")
 
-
 def get_conversational_chain():
-
+    """Create conversation chain with Gemini model"""
     prompt_template = """
     Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
     provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
@@ -52,55 +47,82 @@ def get_conversational_chain():
     Answer:
     """
 
-    model = ChatGoogleGenerativeAI(model="gemini-pro",
-                             temperature=0.3)
-
-    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
 
     return chain
 
-
-
 def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    """Process user questions and generate responses"""
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
 
-    new_db = FAISS.load_local("faiss_index", embeddings)
+    # Load the vector store with safe deserialization
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
     docs = new_db.similarity_search(user_question)
 
     chain = get_conversational_chain()
-
 
     response = chain(
-        {"input_documents":docs, "question": user_question}
-        , return_only_outputs=True)
-
-    print(response)
-    st.write("Reply: ", response["output_text"])
-
+        {"input_documents": docs, "question": user_question},
+        return_only_outputs=True
+    )
 
+    return response["output_text"]
 
+def initialize_session_state():
+    """Initialize session state variables"""
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history = []
+    if 'processing_done' not in st.session_state:
+        st.session_state.processing_done = False
 
 def main():
+    """Main Streamlit application"""
+    initialize_session_state()
+
     st.set_page_config("Chat PDF")
     st.header("Chat with PDF using Gemini💁")
 
-    user_question = st.text_input("Ask a Question from the PDF Files")
-
-    if user_question:
-        user_input(user_question)
-
     with st.sidebar:
         st.title("Menu:")
-        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+        pdf_docs = st.file_uploader(
+            "Upload your PDF Files and Click on the Submit & Process Button",
+            accept_multiple_files=True
+        )
         if st.button("Submit & Process"):
             with st.spinner("Processing..."):
                 raw_text = get_pdf_text(pdf_docs)
                 text_chunks = get_text_chunks(raw_text)
                 get_vector_store(text_chunks)
+                st.session_state.processing_done = True
                 st.success("Done")
 
-
+    # Chat interface
+    if 'processing_done' in st.session_state and st.session_state.processing_done:
+        # Display chat history
+        for message in st.session_state.chat_history:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+        # Chat input
+        if prompt := st.chat_input("Ask a question about your documents"):
+            # Display user message in chat message container
+            with st.chat_message("user"):
+                st.markdown(prompt)
+
+            # Add user message to chat history
+            st.session_state.chat_history.append({"role": "user", "content": prompt})
+
+            # Display assistant response in chat message container
+            with st.chat_message("assistant"):
+                response = user_input(prompt)
+                st.markdown(response)
+
+            # Add assistant response to chat history
+            st.session_state.chat_history.append({"role": "assistant", "content": response})
+    else:
+        st.info("Please upload PDF files and click 'Submit & Process' to start the conversation.")
 
 if __name__ == "__main__":
-    main()
+    main()