Update app.py

app.py

@@ -1,255 +1,234 @@
 import streamlit as st
-import random
-import time
-from streamlit.components.v1 import html
-
-# Set page config with light purple theme
+from streamlit_option_menu import option_menu
+import fitz  # PyMuPDF
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import HuggingFaceHub
+from langchain.chains import RetrievalQA
+import tempfile
+import os
+import base64
+
+# Page configuration
 st.set_page_config(
-    page_title="Emotion Mirror Chatbot",
-    page_icon="😊",
-    layout="centered",
+    page_title="PDF Study Assistant",
+    page_icon="📚",
+    layout="wide",
     initial_sidebar_state="collapsed"
 )
 
-# Custom CSS for enhanced light purple theme
+# Custom CSS for colorful design
 st.markdown("""
 <style>
-:root {
-    --primary: #b19cd9;
-    --primary-dark: #8a7faa;
-    --background: linear-gradient(135deg, #f5f0ff, #e6d7ff);
-    --secondary-background: #e6e0fa;
-    --text: #4a4a4a;
-    --font: "Arial", sans-serif;
-}
-
-body {
-    background-image: var(--background);
-    background-attachment: fixed;
-    color: var(--text);
-    font-family: var(--font);
-}
-
-.stTextInput>div>div>input {
-    background-color: var(--secondary-background) !important;
-    color: var(--text) !important;
-    border: 2px solid var(--primary) !important;
-    border-radius: 12px;
-}
-
-.stButton>button {
-    background-color: var(--primary) !important;
-    color: white !important;
-    border: none;
-    border-radius: 12px;
-    padding: 8px 16px;
-    transition: all 0.3s ease;
-}
-
-.stButton>button:hover {
-    background-color: var(--primary-dark) !important;
-    transform: scale(1.05);
-}
-
-.stMarkdown {
-    font-family: monospace !important;
-    font-size: 16px !important;
-}
-
-.chat-message {
-    padding: 12px 16px;
-    border-radius: 16px;
-    margin: 10px 0;
-    max-width: 80%;
-    box-shadow: 0 2px 6px rgba(0,0,0,0.1);
-}
-
-.user-message {
-    background-color: var(--secondary-background);
-    margin-left: auto;
-    text-align: left;
-    border-bottom-right-radius: 4px;
-}
-
-.bot-message {
-    background-color: var(--primary);
-    color: white;
-    margin-right: auto;
-    border-bottom-left-radius: 4px;
-}
-
-.face-container {
-    text-align: center;
-    padding: 20px;
-    background: rgba(255, 255, 255, 0.7);
-    backdrop-filter: blur(5px);
-    border-radius: 20px;
-    box-shadow: 0 8px 20px rgba(0,0,0,0.1);
-    margin: 20px auto;
-    max-width: 300px;
-    border: 2px solid var(--primary);
-}
-
-.header {
-    text-align: center;
-    margin-bottom: 20px;
-}
-
-.title {
-    color: var(--primary);
-    font-size: 2.5rem;
-    margin-bottom: 10px;
-    text-shadow: 1px 1px 3px rgba(0,0,0,0.1);
-}
-
-.subtitle {
-    color: var(--text);
-    font-size: 1.1rem;
-    margin-bottom: 30px;
-}
-
-.footer {
-    text-align: center;
-    margin-top: 30px;
-    color: var(--primary);
-    font-size: 0.9rem;
-}
+    :root {
+        --primary: #ff4b4b;
+        --secondary: #ff9a3d;
+        --accent1: #ffcb74;
+        --accent2: #3a86ff;
+        --background: #f0f2f6;
+        --card: #ffffff;
+    }
+    
+    .stApp {
+        background: linear-gradient(135deg, var(--background) 0%, #e0e5ec 100%);
+    }
+    
+    .stButton>button {
+        background: linear-gradient(to right, var(--secondary), var(--primary));
+        color: white;
+        border-radius: 12px;
+        padding: 8px 20px;
+        font-weight: 600;
+    }
+    
+    .stTextInput>div>div>input {
+        border-radius: 12px;
+        border: 2px solid var(--accent2);
+        padding: 10px;
+    }
+    
+    .card {
+        background: var(--card);
+        border-radius: 15px;
+        box-shadow: 0 8px 16px rgba(0,0,0,0.1);
+        padding: 20px;
+        margin-bottom: 20px;
+    }
+    
+    .header {
+        background: linear-gradient(to right, var(--accent2), var(--primary));
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        text-align: center;
+        margin-bottom: 30px;
+    }
+    
+    .tab-content {
+        animation: fadeIn 0.5s ease-in-out;
+    }
+    
+    @keyframes fadeIn {
+        from { opacity: 0; }
+        to { opacity: 1; }
+    }
 </style>
 """, unsafe_allow_html=True)
 
-# Emotion databases
-POSITIVE_WORDS = {"happy", "awesome", "great", "joy", "excited", "good", "wonderful", "fantastic", "amazing", "yay", "ecstatic"}
-NEGATIVE_WORDS = {"sad", "depressed", "angry", "cry", "lonely", "bad", "terrible", "awful", "miserable", "upset", "grief"}
-LOVE_WORDS = {"love", "heart", "adore", "crush", "romance", "affection", "passion"}
-HELP_RESPONSES = [
-    "Would you like to talk about it? 💬",
-    "I'm here to listen whenever you need 💙",
-    "Want some uplifting quotes? 📜",
-    "Would a virtual hug help? 🤗",
-    "Let's focus on something positive 🌈",
-    "Remember: this too shall pass 🌤️"
-]
-
-# ASCII Art Library
-FACES = {
-    "happy": r"""
-  ╔════════════╗
-  😄 AWESOME DAY!  
-  ╚════════════╝
-    """,
-    "sad": r"""
-  ╔════════════╗
-  😢 TOUGH TIMES?  
-  ╚════════════╝
-    """,
-    "neutral": r"""
-  ╔════════════╗
-  😐 HELLO THERE   
-  ╚════════════╝
-    """,
-    "love": r"""
-  ╔════════════╗
-  😍 LOVELY FEELING!  
-  ╚════════════╝
-    """,
-    "angry": r"""
-  ╔════════════╗
-  😠 TAKE A DEEP BREATH  
-  ╚════════════╝
-    """
-}
-
-# Confetti effect using JavaScript
-def confetti_effect():
-    confetti_js = """
-    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/confetti.browser.min.js"></script>
-    <script>
-    const count = 200;
-    const defaults = {
-        origin: { y: 0.7 }
-    };
-
-    function fire(particleRatio, opts) {
-        confetti(Object.assign({}, defaults, opts, {
-            particleCount: Math.floor(count * particleRatio),
-            colors: ['#b19cd9', '#e6d7ff', '#8a7faa', '#ffffff']
-        }));
-    }
-
-    fire(0.25, { spread: 26, startVelocity: 55 });
-    fire(0.2, { spread: 60 });
-    fire(0.35, { spread: 100, decay: 0.91, scalar: 0.8 });
-    fire(0.1, { spread: 120, startVelocity: 25, decay: 0.92, scalar: 1.2 });
-    fire(0.1, { spread: 120, startVelocity: 45 });
-    </script>
-    """
-    html(confetti_js)
-
-# Emotion detection function
-def detect_emotion(text):
-    text = text.lower()
-    if any(word in text for word in POSITIVE_WORDS):
-        return "happy"
-    elif any(word in text for word in NEGATIVE_WORDS):
-        return "sad"
-    elif any(word in text for word in LOVE_WORDS):
-        return "love"
-    elif "angry" in text or "mad" in text or "furious" in text:
-        return "angry"
-    return "neutral"
-
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-    st.session_state.current_emotion = "neutral"
-
-# Header with title and description
-st.markdown('<div class="header"><div class="title">✨ Emotion Mirror Chatbot</div><div class="subtitle">I\'m a reactive AI agent that mirrors your emotions! Try words like <i>happy, sad, love,</i> or <i>awesome</i></div></div>', unsafe_allow_html=True)
-
-# Display current face
-with st.container():
-    st.markdown(f"<div class='face-container'>\n{FACES[st.session_state.current_emotion]}\n</div>", 
-                unsafe_allow_html=True)
-
-# Display chat messages
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(f"<div class='chat-message {message['role']}-message'>{message['content']}</div>", 
-                    unsafe_allow_html=True)
-
-# User input
-if prompt := st.chat_input("How are you feeling today?"):
-    # Add user message to chat history
-    st.session_state.messages.append({"role": "user", "content": prompt})
+# Initialize session state
+if 'pdf_processed' not in st.session_state:
+    st.session_state.pdf_processed = False
+if 'qa_chain' not in st.session_state:
+    st.session_state.qa_chain = None
+if 'pages' not in st.session_state:
+    st.session_state.pages = []
+
+# Load models with caching
+@st.cache_resource
+def load_embedding_model():
+    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+@st.cache_resource
+def load_qa_model():
+    return HuggingFaceHub(
+        repo_id="google/flan-t5-xxl",
+        model_kwargs={"temperature": 0.5, "max_length": 512},
+        huggingfacehub_api_token=os.getenv("HF_API_KEY")
+    )
+
+def process_pdf(pdf_file):
+    """Extract text from PDF and create vector store"""
+    with st.spinner("📖 Reading PDF..."):
+        doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
+        text = ""
+        st.session_state.pages = []
+        for page in doc:
+            text += page.get_text()
+            st.session_state.pages.append(page.get_text())
+    
+    with st.spinner("🔍 Processing text..."):
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000,
+            chunk_overlap=200,
+            length_function=len
+        )
+        chunks = text_splitter.split_text(text)
+        
+        embeddings = load_embedding_model()
+        vector_store = FAISS.from_texts(chunks, embeddings)
+        
+        qa_model = load_qa_model()
+        st.session_state.qa_chain = RetrievalQA.from_chain_type(
+            llm=qa_model,
+            chain_type="stuff",
+            retriever=vector_store.as_retriever(search_kwargs={"k": 3}),
+            return_source_documents=True
+        )
+    
+    st.session_state.pdf_processed = True
+    st.success("✅ PDF processed successfully!")
+
+def generate_qa_for_chapter(start_page, end_page):
+    """Generate Q&A for specific chapter pages"""
+    if start_page < 1 or end_page > len(st.session_state.pages) or start_page > end_page:
+        st.error("Invalid page range")
+        return []
     
-    # Detect emotion
-    emotion = detect_emotion(prompt)
-    st.session_state.current_emotion = emotion
+    chapter_text = "\n".join(st.session_state.pages[start_page-1:end_page])
     
-    # Generate bot response
-    if emotion == "happy":
-        response = FACES["happy"] + "\n\n🌟 That's wonderful to hear! Keep spreading positivity!"
-        confetti_effect()
-    elif emotion == "sad":
-        response = FACES["sad"] + "\n\n" + random.choice(HELP_RESPONSES)
-    elif emotion == "love":
-        response = FACES["love"] + "\n\n💖 Love is the most beautiful feeling! Treasure it."
-    elif emotion == "angry":
-        response = FACES["angry"] + "\n\n☁️ Take a deep breath. Count to ten. You've got this."
-    else:
-        response = FACES["neutral"] + "\n\nTell me more about your feelings..."
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=800,
+        chunk_overlap=100,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(chapter_text)
     
-    # Add bot response to chat history
-    st.session_state.messages.append({"role": "bot", "content": response})
+    qa_pairs = []
+    qa_model = load_qa_model()
     
-    # Rerun to update the display
-    st.rerun()
+    with st.spinner(f"🧠 Generating Q&A for pages {start_page}-{end_page}..."):
+        for i, chunk in enumerate(chunks):
+            if i % 2 == 0:  # Generate question
+                prompt = f"Generate a study question based on: {chunk[:500]}"
+                question = qa_model(prompt)[:120] + "?"
+            else:  # Generate answer
+                prompt = f"Answer the question: {qa_pairs[-1][0]} using context: {chunk[:500]}"
+                answer = qa_model(prompt)
+                qa_pairs[-1] = (qa_pairs[-1][0], answer)
+    
+    return qa_pairs
+
+# App header
+st.markdown("<h1 class='header'>📚 PDF Study Assistant</h1>", unsafe_allow_html=True)
+
+# PDF Upload Section
+with st.container():
+    st.subheader("📤 Upload Your Textbook/Notes")
+    pdf_file = st.file_uploader("", type="pdf", label_visibility="collapsed")
 
-# Add reset button
-if st.button("Reset Conversation"):
-    st.session_state.messages = []
-    st.session_state.current_emotion = "neutral"
-    st.rerun()
+# Main content
+if pdf_file:
+    if not st.session_state.pdf_processed:
+        process_pdf(pdf_file)
+    
+    if st.session_state.pdf_processed:
+        # Navigation tabs
+        selected_tab = option_menu(
+            None,
+            ["Ask Questions", "Generate Chapter Q&A"],
+            icons=["chat", "book"],
+            menu_icon="cast",
+            default_index=0,
+            orientation="horizontal",
+            styles={
+                "container": {"padding": "0!important", "background-color": "#f9f9f9"},
+                "nav-link": {"font-size": "16px", "font-weight": "bold"},
+                "nav-link-selected": {"background": "linear-gradient(to right, #3a86ff, #ff4b4b)"},
+            }
+        )
+        
+        # Question Answering Tab
+        if selected_tab == "Ask Questions":
+            st.markdown("### 💬 Ask Questions About Your Document")
+            user_question = st.text_input("Type your question here:", key="user_question")
+            
+            if user_question:
+                with st.spinner("🤔 Thinking..."):
+                    result = st.session_state.qa_chain({"query": user_question})
+                    st.markdown(f"<div class='card'><b>Answer:</b> {result['result']}</div>", unsafe_allow_html=True)
+                    
+                    with st.expander("🔍 See source passages"):
+                        for i, doc in enumerate(result["source_documents"]):
+                            st.markdown(f"**Passage {i+1}:** {doc.page_content[:500]}...")
+        
+        # Chapter Q&A Generation Tab
+        elif selected_tab == "Generate Chapter Q&A":
+            st.markdown("### 📝 Generate Q&A for Specific Chapter")
+            col1, col2 = st.columns(2)
+            with col1:
+                start_page = st.number_input("Start Page", min_value=1, max_value=len(st.session_state.pages), value=1)
+            with col2:
+                end_page = st.number_input("End Page", min_value=1, max_value=len(st.session_state.pages), value=min(5, len(st.session_state.pages)))
+            
+            if st.button("Generate Q&A", key="generate_qa"):
+                qa_pairs = generate_qa_for_chapter(start_page, end_page)
+                
+                if qa_pairs:
+                    st.markdown(f"<h4>📖 Generated Questions for Pages {start_page}-{end_page}</h4>", unsafe_allow_html=True)
+                    for i, (question, answer) in enumerate(qa_pairs):
+                        st.markdown(f"""
+                        <div class='card'>
+                            <b>Q{i+1}:</b> {question}<br>
+                            <b>A{i+1}:</b> {answer}
+                        </div>
+                        """, unsafe_allow_html=True)
+                else:
+                    st.warning("No Q&A pairs generated. Try a different page range.")
 
 # Footer
-st.markdown('<div class="footer">Made with ❤️ | Reactive AI Agent | Streamlit</div>', unsafe_allow_html=True)
+st.markdown("---")
+st.markdown("""
+<div style="text-align: center; padding: 20px;">
+    Built with ❤️ for students | PDF Study Assistant v1.0
+</div>
+""", unsafe_allow_html=True)