Update app.py

app.py

@@ -8,7 +8,7 @@ from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import PromptTemplate
 
-# Hardcoded Gemini API keys
+# Hardcoded Gemini API keys (for fallback)
 API_KEYS = [
     "AIzaSyBYbyC4qCJoKxKR-r0oIn4SVqj4CfSdx4s",
     "AIzaSyBfvYURYVijTurxvFUyV3vZkCojpOAAnFk"
@@ -37,7 +37,6 @@ 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)
-    # Use /tmp for Hugging Face Spaces
     vector_store.save_local("/tmp/faiss_index")
 
 def get_conversational_chain(api_key):
@@ -57,7 +56,6 @@ def get_conversational_chain(api_key):
 
 def user_input(user_question, api_key):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key)
-    # Use /tmp for Hugging Face Spaces
     new_db = FAISS.load_local("/tmp/faiss_index", embeddings, allow_dangerous_deserialization=True)
     docs = new_db.similarity_search(user_question)
     chain = get_conversational_chain(api_key)
@@ -66,27 +64,47 @@ def user_input(user_question, api_key):
 
 def main():
     st.set_page_config(page_title="Chat PDF")
-    st.header("Retrieval-Augmented Generation-Gemini-2.0 Flash")
+    st.header("Retrieval-Augmented Generation - Gemini 2.0 Flash")
     st.markdown("---")
 
-    user_api_key = st.text_input("Enter your API key", type="password")
-    user_question = st.text_input("Ask a question")
+    # STEP 1: Enter API key
+    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
+
+    if not st.session_state["api_entered"]:
+        user_api_key = st.text_input("Enter your Gemini API key", type="password")
+        if st.button("Continue") and user_api_key:
+            st.session_state["user_api_key"] = user_api_key
+            st.session_state["api_entered"] = True
+            st.experimental_rerun()
+        st.stop()
+
+    api_key = st.session_state.get("user_api_key", switch_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"):
+            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()
+            else:
+                st.error("Please upload at least one PDF file.")
+        st.stop()
+
+    # STEP 3: Ask questions
+    st.subheader("Step 3: Ask a question about your PDFs")
+    user_question = st.text_input("Ask a question")
     if user_question:
-        api_key = user_api_key if user_api_key else switch_api_key()
         user_input(user_question, api_key)
 
-    pdf_docs = st.file_uploader("Upload PDF files", accept_multiple_files=True, type=['pdf'])
-    if st.button("Submit & Process"):
-        if pdf_docs:
-            with st.spinner("Processing..."):
-                api_key = user_api_key if user_api_key else switch_api_key()
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks, api_key)
-                st.success("Done")
-        else:
-            st.error("Please upload at least one PDF file.")
-
 if __name__ == "__main__":
-    main()
+    main()