Update app.py

app.py

@@ -2,17 +2,24 @@ import streamlit as st
 from PyPDF2 import PdfReader
 from io import BytesIO
 import os
+import tempfile
 
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+# Fixed imports for LangChain
+from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
 from langchain_community.vectorstores import FAISS
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import PromptTemplate
 
-# --- Get API key from environment variable (set in Hugging Face Secrets or .env file) ---
+# --- Get API key from Hugging Face Secrets ---
+# In Hugging Face Spaces, set this in Settings -> Repository secrets
 GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY", "")
 
+# Use temporary directory for Hugging Face Spaces
+TEMP_DIR = tempfile.gettempdir()
+FAISS_INDEX_PATH = os.path.join(TEMP_DIR, "faiss_index")
+
 def get_pdf_text(pdf_docs):
     text = ""
     for pdf in pdf_docs:
@@ -30,7 +37,7 @@ def get_text_chunks(text):
 def get_vector_store(text_chunks, api_key):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key)
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("/tmp/faiss_index")
+    vector_store.save_local(FAISS_INDEX_PATH)
 
 def get_conversational_chain(api_key):
     prompt_template = """
@@ -42,69 +49,119 @@ def get_conversational_chain(api_key):
     {question}
     Answer:
     """
-    model = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0, google_api_key=api_key)
+    model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-exp", temperature=0, google_api_key=api_key)
     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, api_key):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key)
-    new_db = FAISS.load_local("/tmp/faiss_index", embeddings, allow_dangerous_deserialization=True)
+    new_db = FAISS.load_local(FAISS_INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
     docs = new_db.similarity_search(user_question)
     chain = get_conversational_chain(api_key)
     response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
     st.write("Reply: ", response["output_text"])
 
 def main():
-    st.set_page_config(page_title="Chat PDF")
-    st.header("Retrieval-Augmented Generation - Gemini 2.0")
+    st.set_page_config(page_title="Chat PDF", page_icon="📄")
+    st.header("📚 RAG Chat with PDF - Gemini 2.0")
     st.markdown("---")
 
-    # STEP 1: Use API key from env or ask user
+    # Initialize session state
     if "api_entered" not in st.session_state:
         st.session_state["api_entered"] = False
     if "pdf_processed" not in st.session_state:
         st.session_state["pdf_processed"] = False
 
+    # Check for API key
     api_key = GOOGLE_API_KEY
 
+    # STEP 1: API Key handling
     if not st.session_state["api_entered"]:
         if not api_key:
-            user_api_key = st.text_input("Enter your Gemini API key", type="password")
-            if st.button("Continue") and user_api_key:
+            st.warning(" Google API Key not found in environment variables.")
+            st.info("Please add GOOGLE_API_KEY to your Hugging Face Space secrets or enter it below.")
+            user_api_key = st.text_input("Enter your Gemini API key", type="password", help="Get your API key from https://makersuite.google.com/app/apikey")
+            if st.button("Continue", type="primary") and user_api_key:
                 st.session_state["user_api_key"] = user_api_key
                 st.session_state["api_entered"] = True
-                st.experimental_rerun()
+                st.rerun()
             st.stop()
         else:
             st.session_state["user_api_key"] = api_key
             st.session_state["api_entered"] = True
-            st.experimental_rerun()
 
     api_key = st.session_state.get("user_api_key", "")
 
     # STEP 2: Upload PDF(s)
     if not st.session_state["pdf_processed"]:
-        st.subheader("Step 2: Upload your PDF file(s)")
-        pdf_docs = st.file_uploader("Upload PDF files", accept_multiple_files=True, type=['pdf'])
-        if st.button("Submit & Process PDFs"):
+        st.subheader("📤 Step 1: Upload your PDF file(s)")
+        pdf_docs = st.file_uploader(
+            "Upload PDF files", 
+            accept_multiple_files=True, 
+            type=['pdf'],
+            help="Select one or more PDF files to analyze"
+        )
+        
+        if st.button("Submit & Process PDFs", type="primary", disabled=not pdf_docs):
             if pdf_docs:
-                with st.spinner("Processing..."):
-                    raw_text = get_pdf_text(pdf_docs)
-                    text_chunks = get_text_chunks(raw_text)
-                    get_vector_store(text_chunks, api_key)
-                    st.session_state["pdf_processed"] = True
-                    st.success("PDFs processed! You can now ask questions.")
-                    st.experimental_rerun()
+                with st.spinner("Processing PDFs... This may take a moment."):
+                    try:
+                        raw_text = get_pdf_text(pdf_docs)
+                        if not raw_text.strip():
+                            st.error(" No text could be extracted from the PDF(s). Please check your files.")
+                            st.stop()
+                        
+                        text_chunks = get_text_chunks(raw_text)
+                        get_vector_store(text_chunks, api_key)
+                        st.session_state["pdf_processed"] = True
+                        st.success(" PDFs processed successfully! You can now ask questions.")
+                        st.rerun()
+                    except Exception as e:
+                        st.error(f" Error processing PDFs: {str(e)}")
+                        st.stop()
             else:
                 st.error("Please upload at least one PDF file.")
+        
+        if not pdf_docs:
+            st.info(" Please upload one or more PDF files to get started.")
+        
         st.stop()
 
     # STEP 3: Ask questions
-    st.subheader("Step 3: Ask a question about your PDFs")
-    user_question = st.text_input("Ask a question")
+    st.subheader(" Step 2: Ask questions about your PDFs")
+    
+    # Add a reset button
+    col1, col2 = st.columns([3, 1])
+    with col2:
+        if st.button(" Upload New PDFs"):
+            st.session_state["pdf_processed"] = False
+            st.rerun()
+    
+    # Question input
+    user_question = st.text_input(
+        "Ask a question about your uploaded PDFs",
+        placeholder="e.g., What are the main topics discussed in the document?",
+        help="The AI will only answer based on the content of your uploaded PDFs"
+    )
+    
     if user_question:
-        user_input(user_question, api_key)
+        with st.spinner("Searching for answer..."):
+            try:
+                user_input(user_question, api_key)
+            except Exception as e:
+                st.error(f" Error getting answer: {str(e)}")
+
+    # Add footer
+    st.markdown("---")
+    st.markdown(
+        """
+        <div style='text-align: center'>
+            <small>Built with Streamlit, LangChain, and Google Gemini 2.0</small>
+        </div>
+        """, 
+        unsafe_allow_html=True
+    )
 
 if __name__ == "__main__":
-    main()
+    main()