Update app.py

app.py

@@ -13,7 +13,6 @@ from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import PromptTemplate
 
 # --- 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
@@ -23,6 +22,7 @@ FAISS_INDEX_PATH = os.path.join(TEMP_DIR, "faiss_index")
 # PDF file path
 PDF_FILE_PATH = "./slide.pdf"
 
+
 def get_pdf_text_from_file(pdf_path):
     """Read PDF from file path"""
     text = ""
@@ -36,40 +36,91 @@ def get_pdf_text_from_file(pdf_path):
             text += page_text
     return text
 
+
 def get_text_chunks(text):
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
     return text_splitter.split_text(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(FAISS_INDEX_PATH)
 
+
 def get_conversational_chain(api_key):
     prompt_template = """
-    You are a helpful assistant that only answers based on the context provided from the PDF documents.
-    Do not use any external knowledge or assumptions. If the answer is not found in the context below, reply with "I don't know."
-    Context:
+    You are a helpful assistant for Antimicrobial Pharmacology. You answer questions based ONLY on the context provided from the PDF documents.
+    
+    IMPORTANT RULES:
+    1. Do not use any external knowledge or assumptions.
+    2. If the answer is not found in the context, reply with "I don't know based on the provided materials."
+    3. Be conversational and helpful.
+    4. When generating MCQs, create questions that test understanding of the material.
+    5. When checking answers, be encouraging and provide explanations.
+    
+    Chat History:
+    {chat_history}
+    
+    Context from PDF:
     {context}
-    Question:
+    
+    Current Question:
     {question}
+    
+    Instructions:
+    - If the user asks for a multiple choice question (MCQ), quiz, or test question:
+      * Generate a question with 4 options (A, B, C, D) based ONLY on the context
+      * Make sure the question tests important concepts from the material
+      * Do NOT reveal the correct answer yet
+      * Ask the user to select their answer
+    
+    - If the user provides an answer (like "A", "B", "C", "D" or the full answer text) AND there was a recent MCQ in the chat history:
+      * Check if the answer is correct based on the context
+      * If correct: Congratulate them and explain why it's correct
+      * If incorrect: Encourage them, reveal the correct answer, and explain why
+    
+    - For regular questions: Answer based on the context provided
+    
     Answer:
     """
-    model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-exp", temperature=0, google_api_key=api_key)
-    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-exp", temperature=0.3, google_api_key=api_key)
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question", "chat_history"])
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
     return chain
 
-def user_input(user_question, api_key):
+
+def get_response(user_question, api_key, chat_history):
+    """Get response from the AI model with chat history context"""
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key)
     new_db = FAISS.load_local(FAISS_INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
-    docs = new_db.similarity_search(user_question)
+    docs = new_db.similarity_search(user_question, k=4)
+    
+    # Format chat history for context
+    history_text = ""
+    for msg in chat_history[-10:]:  # Keep last 10 messages for context
+        role = "User" if msg["role"] == "user" else "Assistant"
+        history_text += f"{role}: {msg['content']}\n"
+    
     chain = get_conversational_chain(api_key)
-    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
-    st.write("Reply: ", response["output_text"])
+    response = chain(
+        {
+            "input_documents": docs, 
+            "question": user_question,
+            "chat_history": history_text
+        }, 
+        return_only_outputs=True
+    )
+    return response["output_text"]
+
 
 def main():
-    st.set_page_config(page_title="Antimicrobial Pharmacology Chatbot", page_icon="")
+    st.set_page_config(
+        page_title="Antimicrobial Pharmacology Chatbot", 
+        page_icon="",
+        layout="wide"
+    )
+    
     st.header("Antimicrobial Pharmacology Chatbot (RX24)")
     st.markdown("---")
 
@@ -78,6 +129,8 @@ def main():
         st.session_state["api_entered"] = False
     if "pdf_processed" not in st.session_state:
         st.session_state["pdf_processed"] = False
+    if "messages" not in st.session_state:
+        st.session_state["messages"] = []
 
     # Check for API key
     api_key = GOOGLE_API_KEY
@@ -85,13 +138,17 @@ def main():
     # STEP 1: API Key handling
     if not st.session_state["api_entered"]:
         if not api_key:
-            st.warning(" Google API Key not found in environment variables.")
+            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")
+            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
@@ -107,7 +164,7 @@ def main():
             try:
                 raw_text = get_pdf_text_from_file(PDF_FILE_PATH)
                 if not raw_text.strip():
-                    st.error(" No text could be extracted from the PDF. Please check your file.")
+                    st.error("No text could be extracted from the PDF. Please check your file.")
                     st.stop()
                 
                 st.info(f"Processing: {PDF_FILE_PATH}")
@@ -115,52 +172,120 @@ def main():
                 text_chunks = get_text_chunks(raw_text)
                 get_vector_store(text_chunks, api_key)
                 st.session_state["pdf_processed"] = True
-                st.success(" PDF processed successfully! You can now ask questions.")
-                st.experimental_rerun()
+                st.success("PDF processed successfully. You can now ask questions.")
+                st.rerun()
             except FileNotFoundError as e:
-                st.error(f" {str(e)}")
+                st.error(str(e))
                 st.stop()
             except Exception as e:
-                st.error(f" Error processing PDF: {str(e)}")
+                st.error(f"Error processing PDF: {str(e)}")
                 st.stop()
 
-    # STEP 3: Ask questions
-    st.subheader(" Ask questions about Antimicrobial Pharmacology Text")
-    
-    # Show loaded file
-    st.info(f"Loaded Antimicrobial Pharmacology Course")
-    
-    # Add a reprocess button
-    col1, col2 = st.columns([3, 1])
-    with col2:
-        if st.button(" Reprocess PDF"):
+    # STEP 3: Chat Interface
+    # Sidebar with options
+    with st.sidebar:
+        st.subheader("Options")
+        st.info("Loaded: Antimicrobial Pharmacology Course")
+        
+        if st.button("Reprocess PDF"):
             st.session_state["pdf_processed"] = False
-            st.experimental_rerun()
-    
-    # Question input
-    user_question = st.text_input(
-        "Ask a question about your Antimicrobial Pharmacology Course",
-        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"
-    )
+            st.rerun()
+        
+        if st.button("Clear Chat History"):
+            st.session_state["messages"] = []
+            st.rerun()
+        
+        st.markdown("---")
+        st.subheader("Quick Actions")
+        st.markdown("""
+        Try asking:
+        - "Give me a multiple choice question"
+        - "Quiz me on antibiotics"
+        - "Generate an MCQ about [topic]"
+        - "What are the main topics?"
+        """)
+        
+        st.markdown("---")
+        st.subheader("How to use MCQs")
+        st.markdown("""
+        1. Ask for an MCQ (e.g., "Give me a quiz question")
+        2. The bot will generate a question with options A-D
+        3. Reply with your answer (e.g., "A" or "The answer is B")
+        4. Get feedback on whether you're correct
+        """)
+
+    # Main chat area
+    st.subheader("Chat with your Pharmacology Assistant")
     
-    if user_question:
-        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)}")
+    # Display chat messages
+    for message in st.session_state["messages"]:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Quick action buttons (only show if no messages yet)
+    if len(st.session_state["messages"]) == 0:
+        st.markdown("### Quick Start")
+        col1, col2, col3 = st.columns(3)
+        
+        with col1:
+            if st.button("Generate MCQ", use_container_width=True):
+                quick_question = "Give me a multiple choice question to test my knowledge"
+                st.session_state["messages"].append({"role": "user", "content": quick_question})
+                with st.spinner("Generating question..."):
+                    response = get_response(quick_question, api_key, st.session_state["messages"])
+                    st.session_state["messages"].append({"role": "assistant", "content": response})
+                st.rerun()
+        
+        with col2:
+            if st.button("Summarize Topics", use_container_width=True):
+                quick_question = "What are the main topics covered in this material?"
+                st.session_state["messages"].append({"role": "user", "content": quick_question})
+                with st.spinner("Analyzing..."):
+                    response = get_response(quick_question, api_key, st.session_state["messages"])
+                    st.session_state["messages"].append({"role": "assistant", "content": response})
+                st.rerun()
+        
+        with col3:
+            if st.button("How can you help?", use_container_width=True):
+                quick_question = "What can you help me with regarding this pharmacology material?"
+                st.session_state["messages"].append({"role": "user", "content": quick_question})
+                with st.spinner("Processing..."):
+                    response = get_response(quick_question, api_key, st.session_state["messages"])
+                    st.session_state["messages"].append({"role": "assistant", "content": response})
+                st.rerun()
+
+    # Chat input
+    if user_question := st.chat_input("Ask a question or answer an MCQ..."):
+        # Add user message to chat history
+        st.session_state["messages"].append({"role": "user", "content": user_question})
+        
+        # Display user message
+        with st.chat_message("user"):
+            st.markdown(user_question)
+        
+        # Generate and display assistant response
+        with st.chat_message("assistant"):
+            with st.spinner("Processing..."):
+                try:
+                    response = get_response(user_question, api_key, st.session_state["messages"])
+                    st.markdown(response)
+                    st.session_state["messages"].append({"role": "assistant", "content": response})
+                except Exception as e:
+                    error_msg = f"Error getting answer: {str(e)}"
+                    st.error(error_msg)
+                    st.session_state["messages"].append({"role": "assistant", "content": error_msg})
 
     # Add footer
     st.markdown("---")
     st.markdown(
         """
         <div style='text-align: center'>
-            <small></small>
+            <small>Antimicrobial Pharmacology Chatbot - Powered by Gemini AI</small>
         </div>
         """, 
         unsafe_allow_html=True
     )
 
+
 if __name__ == "__main__":
     main()