Create app.py

app.py

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+import numpy as np
+import tempfile
+import soundfile as sf
+import gradio as gr
+import json
+import random
+import os
+import logging
+from google.api_core.exceptions import GoogleAPIError, NotFound, PermissionDenied
+from google import genai
+from google.genai import types
+
+from langchain.output_parsers import StructuredOutputParser, ResponseSchema
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.documents import Document
+from langchain_core.output_parsers import StrOutputParser
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_core.runnables import RunnablePassthrough
+from langchain.retrievers.multi_query import MultiQueryRetriever
+from langchain.chains.retrieval import create_retrieval_chain
+from langchain_core.output_parsers import JsonOutputParser
+from langchain.chains.combine_documents import create_stuff_documents_chain
+
+# ✅ Hugging Face environment secret
+API_KEY = os.environ.get("GOOGLE_API_KEY")
+client = genai.Client(api_key=API_KEY)
+
+
+# ------------- Global State -------------
+full_transcript = []
+lecture_index = []
+vector_db = None
+rag_chain = None
+current_correct_answer = ""
+
+# ------------- Transcript State Management -------------
+def get_full_transcript_text():
+    return " ".join(full_transcript)
+
+def clear_transcript_data():
+    global full_transcript
+    full_transcript = []
+    print("Transcript data cleared.")
+    return ""
+
+# ------------- Gemini STT Function -------------
+def transcribe_audio_chunk(audio_path):
+    try:
+        client = genai.Client(api_key=API_KEY)
+        uploaded_file = client.files.upload(file=audio_path)
+        print(f"✅ File uploaded. URI: {uploaded_file.uri}, Name: {uploaded_file.name}")
+
+        prompt = (
+            "Please perform speech-to-text transcription for the provided audio file. "
+            "Output the transcribed text followed by the key points as a numbered list. "
+            "Do not use any JSON formatting—just return plain text."
+        )
+
+        print("🚀 Sending transcription request to Gemini...")
+        response = client.models.generate_content(
+            model='gemini-2.0-flash',
+            contents=[prompt, uploaded_file]
+        )
+
+        if not response.candidates:
+            block_reason = response.prompt_feedback.block_reason if response.prompt_feedback else "Unknown"
+            return f"Transcription failed. Block Reason: {block_reason}"
+
+        candidate = response.candidates[0]
+        if hasattr(candidate.content, 'parts') and candidate.content.parts:
+            transcript = candidate.content.parts[0].text
+            print("✅ Transcription successful.")
+            return transcript
+        else:
+            return "Error: Failed to parse transcription response."
+
+    except PermissionDenied as e:
+        return f"❌ Permission Denied: {e.message}"
+    except NotFound as e:
+        return f"❌ Resource Not Found: {e.message}"
+    except GoogleAPIError as e:
+        return f"❌ API Error: {e.message}"
+    except Exception as e:
+        return f"❌ Unexpected Error: {str(e)}"
+
+# ------------- Formatting the Output -------------
+def format_transcription_result(result_text):
+    return result_text
+
+# ------------- Gradio Transcription Handler -------------
+def handle_transcription_request(audio_file):
+    if audio_file is None:
+        return "", get_full_transcript_text(), gr.update(value=None), "Transcription not initiated.", "Input declined. No audio file provided."
+
+    transcript_text = transcribe_audio_chunk(audio_file)
+    formatted_chunk = format_transcription_result(transcript_text)
+    full_transcript.append(formatted_chunk)
+
+    return (
+        formatted_chunk,
+        get_full_transcript_text(),
+        gr.update(value=None),
+        "Transcription successful.",
+        "Input accepted. Audio file is being processed."
+    )
+
+def handle_clear_transcript():
+    clear_transcript_data()
+    return "", "Transcript cleared."
+
+
+# ------------- Chunking, Embedding, Vector DB & RAG -------------
+
+def chunk_transcript(text, chunk_size: int = 800, overlap_size: int = 150):
+    # Optionally, you could call: text = correct_transcript_errors(text)
+    document = [Document(page_content=text)]
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=overlap_size
+    )
+    chunks = splitter.split_documents(documents=document)
+    print(f"File split into {len(chunks)} chunks.")
+    return chunks
+
+def create_vector_db(text_chunks, collection_name="transcription-rag"):
+    global vector_db
+    try:
+        embeddings = GoogleGenerativeAIEmbeddings(model="models/text-embedding-004", google_api_key=API_KEY)
+        vector_db = Chroma.from_documents(
+            documents=text_chunks,
+            embedding=embeddings,
+            collection_name=collection_name,
+            persist_directory="/content/chroma_db"  # Ephemeral persist directory.
+        )
+        print(f"Vector DB created with collection_name: {collection_name}")
+        return vector_db
+    except Exception as e:
+        raise Exception(f"Error creating vector DB: {str(e)}")
+
+def setup_rag_chain(vector_db):
+    if not vector_db:
+        raise ValueError("Vector DB not initialized!")
+
+    try:
+        llm = ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash",
+            temperature=0.1,
+            max_tokens=None,
+            timeout=None,
+            max_retries=2,
+            google_api_key=API_KEY
+        )
+
+        # Few-shot query rewriting prompt
+        query_prompt = PromptTemplate.from_template("""
+            You are an AI assistant that helps rephrase queries.
+
+            Example 1:
+            Original Question: Who is Master Sito?
+            Alternative Queries:
+              1. According to the transcript, what is Master Sito's role?
+              2. What does the transcript state about Master Sito?
+              3. How is Master Sito described in the lecture?
+
+            Example 2:
+            Original Question: Who is Master Sito?
+            Even if the transcript contains a minor typo (e.g., 'Master Ceto'),
+            assume the intended name is Master Sito.
+
+            Now, given the original question: {question}
+            Generate three alternative queries:
+        """)
+
+        retriever = MultiQueryRetriever.from_llm(
+            retriever=vector_db.as_retriever(search_kwargs={"k": 4}),
+            llm=llm,
+            prompt=query_prompt
+        )
+
+        # Main prompt for answering with grounding and few-shot examples
+        main_template = """
+            You are an educational assistant. Answer the user's question based solely on the transcript context provided.
+            Disregard minor transcription errors (for example, if the transcript has "Master Ceto" but context indicates it should be "Master Sito").
+            If the answer is explicitly stated, provide it exactly. Otherwise, reply "I don’t know."
+
+            Few-shot examples:
+            ---------------------
+            Transcript Example 1:
+            "Master Sito said: 'Face life with humor.'"
+            Q: What did Master Sito say about life?
+            A: Face life with humor.
+            ---------------------
+            Transcript Example 2:
+            "According to the lecture, Master Sito is a monk living in seclusion."
+            Q: Who is Master Sito?
+            A: He is a monk.
+            ---------------------
+            Now, using the transcript below:
+            Transcript:
+            {context}
+
+            Question: {question}
+            Answer:
+        """
+
+        prompt = ChatPromptTemplate.from_template(template=main_template)
+
+        chain = (
+            {"context": retriever, "question": RunnablePassthrough()}
+            | prompt
+            | llm
+            | StrOutputParser()
+        )
+
+        print("RAG chain setup complete!")
+        return chain
+
+    except Exception as e:
+        raise Exception(f"Error setting up RAG chain: {str(e)}")
+
+def handle_indexing_request(transcript_text):
+    global vector_db, rag_chain
+    if not transcript_text or len(transcript_text.strip()) == 0:
+        return "⚠️ Transcript is empty. Please transcribe or paste something first."
+    try:
+        chunks = chunk_transcript(transcript_text)
+        vector_db = create_vector_db(chunks)
+        rag_chain = setup_rag_chain(vector_db)
+        return f"✅ Indexing complete. {len(chunks)} chunks indexed."
+    except Exception as e:
+        return f"❌ Indexing failed: {str(e)}"
+
+def query(chain, question: str):
+    if not chain:
+        print("RAG chain not initialized!")
+    try:
+        return chain.invoke(question)
+    except Exception as e:
+        raise Exception(f"Error processing query: {str(e)}")
+
+def answer_query_using_rag(user_query):
+    global rag_chain
+    if not rag_chain:
+        return "⚠️ Please index the transcript first."
+    try:
+        result = query(rag_chain, user_query)
+        return f"💬 {result}"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+        #-------------Quiz Generation with few shot prompting---------------------------------------------
+def setup_quiz_chain():
+    try:
+        llm_quiz = ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash",
+            temperature=0.1,
+            # Consider setting a reasonable max_tokens limit, e.g., max_tokens=1024
+            max_tokens=None,
+            # Consider setting an explicit timeout, e.g., timeout=120
+            timeout=None,
+            max_retries=2,
+            google_api_key=API_KEY
+        )
+
+        quiz_template = """
+            You are an educational assistant. Your task is to generate 5 multiple-choice quiz questions based only on the transcript provided below.
+            Please return the output strictly as valid JSON. Do not include any introductory text or markdown formatting around the JSON object.
+            The JSON should be a list containing 5 objects, each following this format:
+
+            {{
+              "question": "Your quiz question here.",
+              "options": ["Option A", "Option B", "Option C", "Option D"],
+              "answer": "The correct option (must exactly match one of the options)"
+            }}
+
+            Transcript:
+            {transcript}
+
+            JSON Output:
+        """ # Added "JSON Output:" hint and refined instructions slightly
+
+        quiz_prompt = PromptTemplate.from_template(quiz_template)
+        # For standard JSON:
+        parser = JsonOutputParser()
+
+        # Update the chain to use the JsonOutputParser
+        chain = (
+            {"transcript": RunnablePassthrough()}
+            | quiz_prompt
+            | llm_quiz
+            | parser # <-- Use JsonOutputParser instead of StrOutputParser
+        )
+        print("Quiz chain setup complete!")
+        return chain
+
+    except Exception as e:
+        raise Exception(f"Error setting up Quiz chain: {str(e)}")
+
+
+# --- Global Quiz State ---
+quiz_state = None
+
+# --- Function to Generate Quiz ---
+def generate_quiz(transcript: str):
+    global quiz_state
+    if not transcript or transcript.strip() == "":
+        return "⚠️ Please provide a transcript.", [], "No quiz generated."
+    try:
+        chain = setup_quiz_chain()
+        output = chain.invoke({"transcript": transcript})
+        print("DEBUG - Chain output:", output)
+        quiz_data = output  # Already parsed JSON from JsonOutputParser.
+    except Exception as e:
+        return f"Quiz generation failed: {str(e)}", [], "Error occurred."
+    if not quiz_data or len(quiz_data) == 0:
+        return "⚠️ No quiz questions returned by the model.", [], ""
+
+    # Initialize quiz state with an additional 'answered' flag.
+    quiz_state = {
+    "questions": quiz_data,
+    "current_index": 0,
+    "score": 0,
+    "streak": 0,            # New: Track consecutive correct answers.
+    "answered": False       # New: Flag to indicate if the current question is answered.
+}
+
+    first_question = quiz_data[0]
+    return first_question["question"], first_question["options"], ""
+
+# --- Function to Evaluate Answer (without advancing to next question) ---
+def select_answer(index: int):
+    global quiz_state
+    if not quiz_state or "questions" not in quiz_state:
+        return "No quiz generated. Please generate a quiz first.", "N/A", "N/A", "N/A", "N/A", "⚠️", "Score: 0 | Streak: 0"
+
+    # Prevent re-answering if the question was already answered.
+    if quiz_state.get("answered", False):
+        current_question = quiz_state["questions"][quiz_state["current_index"]]
+        options = current_question.get("options", [])
+        btn_labels = [options[i] if i < len(options) else "N/A" for i in range(4)]
+        return (current_question["question"], btn_labels[0], btn_labels[1], btn_labels[2], btn_labels[3],
+                "You have already answered. Click 'Next Question' to continue.",
+                f"Score: {quiz_state.get('score', 0)} | Streak: {quiz_state.get('streak', 0)}")
+
+    current_question = quiz_state["questions"][quiz_state["current_index"]]
+    options = current_question.get("options", [])
+    if index >= len(options):
+        return "Invalid option selected.", "N/A", "N/A", "N/A", "N/A", "Error", f"Score: {quiz_state.get('score', 0)} | Streak: {quiz_state.get('streak', 0)}"
+
+    selected_option = options[index]
+
+    # Check answer and update score and streak.
+    if selected_option == current_question["answer"]:
+        feedback = "Correct!"
+        quiz_state["score"] += 1
+        quiz_state["streak"] += 1
+    else:
+        feedback = f"Incorrect. The correct answer was: {current_question['answer']}."
+        quiz_state["streak"] = 0
+
+    quiz_state["answered"] = True  # Mark the question as answered.
+    btn_labels = [options[i] if i < len(options) else "N/A" for i in range(4)]
+    score_text = f"Score: {quiz_state['score']} | Streak: {quiz_state['streak']}"
+    return (current_question["question"], btn_labels[0], btn_labels[1], btn_labels[2], btn_labels[3],
+            feedback, score_text)
+
+# --- Function to Advance to the Next Question ---
+def advance_to_next_question():
+    global quiz_state
+    if not quiz_state or "questions" not in quiz_state:
+        return "No quiz generated. Please generate a quiz first.", "N/A", "N/A", "N/A", "N/A", "⚠️", "Score: 0 | Streak: 0"
+
+    if not quiz_state.get("answered", False):
+        return "Please select an answer before proceeding.", "N/A", "N/A", "N/A", "N/A", "⚠️", f"Score: {quiz_state['score']} | Streak: {quiz_state['streak']}"
+
+    quiz_state["current_index"] += 1
+    quiz_state["answered"] = False  # Reset the answered flag.
+    if quiz_state["current_index"] < len(quiz_state["questions"]):
+        next_q = quiz_state["questions"][quiz_state["current_index"]]
+        options = next_q.get("options", [])
+        btn_labels = [options[i] if i < len(options) else "N/A" for i in range(4)]
+        return (next_q["question"], btn_labels[0], btn_labels[1], btn_labels[2], btn_labels[3],
+                "", f"Score: {quiz_state['score']} | Streak: {quiz_state['streak']}")
+    else:
+        score = quiz_state["score"]
+        total = len(quiz_state["questions"])
+        percentage = round((score / total) * 100)
+        color = "red" if percentage < 60 else "green"
+        # Display final score with some HTML styling.
+        percent_display = f"<span style='color:{color}; font-weight:bold;'>{percentage}%</span>"
+        final_msg = f"Quiz complete! Your final score is {score} out of {total}: {percent_display}."
+        quiz_state = None
+        return final_msg, "", "", "", "", "", ""
+
+
+# --- Combined function to update quiz question & button labels on generation ---
+def generate_quiz_and_buttons(transcript: str):
+    question, options, feedback = generate_quiz(transcript)
+    btn_labels = ["N/A", "N/A", "N/A", "N/A"]
+    if isinstance(options, list):
+        for i in range(min(len(options), 4)):
+            btn_labels[i] = options[i]
+    score_text = "Score: 0 | Streak: 0"
+    return question, btn_labels[0], btn_labels[1], btn_labels[2], btn_labels[3], feedback, score_text
+
+def select_answer_and_update(index: int):
+    # (Call our select_answer function.)
+    return select_answer(index)
+
+def load_transcript(full_text):
+    # For now, simply return the same text.
+    # Adjust this function based on your intended behavior.
+    return full_text
+
+def clear_transcript():
+    # This dummy implementation clears the transcript and returns a cleared status message.
+    return "", "Transcript cleared."
+
+def handle_query_request(user_query):
+    if not user_query or not user_query.strip():
+        return "⚠️ Please enter a valid question about the lecture."
+
+    # Hypothetical function that uses your indexed transcript + LLM:
+    return answer_query_using_rag(user_query)
+
+    # ------------------ Gradio Interface with Custom Retro Theme ------------------
+with gr.Blocks(
+    theme="d8ahazard/material_design_rd",
+    css="""
+        @import url('https://fonts.cdnfonts.com/css/minecraft-4');
+        @import url('https://fonts.googleapis.com/css2?family=Press+Start+2P&display=swap');
+
+        /* Universal (normal) font styles */
+        body,
+        .gradio-container,
+        .gr-button,
+        .gr-markdown,
+        .gr-textbox,
+        h1, h2, h3, h4, p {
+            font-family: "Arial", sans-serif !important;
+            color: #fff !important;
+            letter-spacing: 0.05em;
+            line-height: 1.6;
+            background-color: #101010 !important;
+            margin: 0;
+            padding: 0;
+        }
+
+        /* Accent color definition for buttons and highlights */
+        .accent-bg {
+            background-color: #8e44ad !important;
+            font-family: 'Press Start 2P', monospace !important;
+            color: #000 !important;
+            text-shadow: none;
+        }
+
+        .accent-bg:hover {
+            background-color: #9c59bd !important;
+        }
+
+        /* Special Minecraft heading class (for header only) */
+        .minecraft-heading {
+            font-family: 'Minecraft', sans-serif !important;
+            letter-spacing: 0.15em;
+            font-size: 28px;
+        }
+
+        /* Typewriter effect for header */
+        .typewriter {
+            overflow: hidden;
+            border-right: 0.15em solid #fff;
+            white-space: nowrap;
+            animation: typing 2.5s steps(30, end), blink-caret 0.75s step-end infinite;
+            width: fit-content;
+            font-weight: 700;
+            line-height: 1.8;
+        }
+
+        @keyframes typing {
+            from { width: 0; }
+            to { width: 100%; }
+        }
+
+        @keyframes blink-caret {
+            from, to { border-color: transparent; }
+            50% { border-color: #fff; }
+        }
+
+        /* Bot logo with bounce animation */
+        #bot-logo {
+            animation: bounce 1.2s ease infinite !important;
+            border-radius: 8px;
+            width: 90px;
+            height: 90px;
+            object-fit: contain;
+            margin-right: 8px;
+        }
+
+        @keyframes bounce {
+            0%, 100% { transform: translateY(0); }
+            50% { transform: translateY(-8px); }
+        }
+
+        /* Dark-themed textboxes for consistency */
+        .gr-textbox, .gr-textbox textarea, .gr-textbox input {
+            background-color: #2b2b2b !important;
+            color: #fff !important;
+            border: 1px solid #555 !important;
+            border-radius: 4px;
+            padding: 4px 8px;
+        }
+
+        ::placeholder {
+            color: #aaa !important;
+            opacity: 1;
+        }
+
+        /* Additional spacing for header and elements */
+        #header {
+            margin-bottom: 16px;
+        }
+
+        .tab-content {
+            padding: 16px;
+        }
+
+        /* Retro-styled scoreboard for Quiz Generator */
+        #quiz-scoreboard {
+            border: 2px solid #0aff0a;
+            padding: 8px;
+            margin-bottom: 8px;
+            font-family: 'Press Start 2P', monospace;
+            color: #0aff0a;
+            background-color: #000;
+            text-align: right;
+        }
+
+        /* Retro panel style (for prompts or feedback) with green fonts */
+        .retro-panel {
+            border: 2px solid #ff66ff;
+            background-color: #111;
+            padding: 8px;
+            margin-bottom: 8px;
+            font-family: 'Press Start 2P', monospace;
+            color: #0aff0a !important;
+            text-align: center;
+            text-shadow: 0 0 4px #0aff0a, 0 0 8px #0aff0a;
+        }
+
+        /* Neon text glow effect for headings and other elements */
+        .neon-text {
+            text-shadow: 0 0 4px #ff66ff, 0 0 8px #ff66ff;
+        }
+
+        /* Neon style for tab labels */
+        .gradio-container .tabs button {
+            font-family: 'Press Start 2P', monospace !important;
+            color: #ff66ff !important;
+            text-shadow: 0 0 4px #ff66ff, 0 0 8px #ff66ff;
+            background-color: transparent !important;
+            border: none !important;
+        }
+
+        .gradio-container .tabs button:hover {
+            background-color: #333 !important;
+        }
+
+        .gradio-container .tabs button.selected {
+            color: #0aff0a !important;
+            text-shadow: 0 0 4px #0aff0a, 0 0 8px #0aff0a;
+        }
+
+        /* --- START: Added CSS to hide Audio elements --- */
+        .gradio-audio .file-drop svg {
+            display: none !important;
+        }
+
+        /* Optional: Hide the "Drop Audio Here" text as well */
+        /*
+        .gradio-audio .file-drop span {
+            display: none !important;
+        }
+        */
+        /* --- END: Added CSS to hide Audio elements --- */
+    """
+) as app:
+
+
+    # Link Minecraft font and Press Start 2P for retro elements
+    gr.Markdown('<link href="https://fonts.cdnfonts.com/css/minecraft-4" rel="stylesheet">')
+    gr.Markdown('<link href="https://fonts.googleapis.com/css2?family=Press+Start+2P&display=swap" rel="stylesheet">')
+
+    # -- Header with Minecraft only on the H2 --
+    with gr.Row():
+         with gr.Column(scale=0, min_width=90):
+              # gr.HTML(""" <img src='/kaggle/input/bot1-jpg/Assistant (1).jpg' id='bot-logo'>""")
+                gr.Image(value="/kaggle/input/bot1-jpg/Assistant (1).jpg", show_label=False, elem_id="bot-logo", height=90)
+              # gr.HTML("""<img id="bot-logo" src="/kaggle/input/bot1-jpg/Assistant (1).jpg" alt="Bot" />""")
+              # gr.Markdown("### My Bot is here!")
+         with gr.Column(scale=1):
+            gr.Markdown(
+                """
+                <h2 class="minecraft-heading typewriter neon-text" style="margin: 0;">
+                    Inclusive Classroom Assistant
+                </h2>
+                <p class="neon-text" style="margin: 4px 0 0 0; font-size: 14px;">
+                    Upload audio, transcribe, index, and ask anything about your lecture!
+                </p>
+                """,
+                elem_id="header"
+            )
+
+    # ------------------ Tab 1: Transcription & Indexing ------------------
+    with gr.Tab("🎙️ Transcription & Indexing") as tab1:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("<h3 class='neon-text'>Transcription Input</h3>")
+                audio_input = gr.Audio(type="filepath", show_label=False)
+                transcribe_button = gr.Button("Transcribe Chunk", elem_classes="accent-bg")
+                transcription_input_status_textbox = gr.Textbox(label="Transcription Input Status", lines=1, interactive=False)
+                latest_chunk_textbox = gr.Textbox(label="Latest Transcript Chunk", lines=10, interactive=False)
+                status_textbox = gr.Textbox(label="Status", lines=1, interactive=False)
+            with gr.Column(scale=1):
+                gr.Markdown("<h3 class='neon-text'>Full Transcript & Indexing</h3>")
+                full_transcript_textbox = gr.Textbox(label="Full Lecture Transcript", lines=20, interactive=False)
+                with gr.Row():
+                    index_button = gr.Button("Index Transcript for Search", elem_classes="accent-bg")
+                    clear_button = gr.Button("Clear Full Transcript", elem_classes="accent-bg")
+                indexing_status_display = gr.Textbox(label="Indexing Status", lines=2, interactive=False)
+
+    # ------------------ Tab 2: Query Lecture Content ------------------
+    with gr.Tab("💬 Query Lecture Content") as tab2:
+        gr.Markdown("<h3 class='neon-text'>Ask a question about the lecture content</h3>")
+        with gr.Row():
+            query_input_textbox = gr.Textbox(
+                label="Ask a question",
+                placeholder="E.g., Ask a question related to the lecture?",
+                lines=2
+            )
+            ask_button = gr.Button("Ask Question", elem_classes="accent-bg")
+        # Answer display with neon and retro effects
+        answer_display = gr.Markdown(
+            "💡 Answer will appear here...",
+            elem_classes="query-answer-box retro-panel neon-text",
+            # label="Answer" # Markdown doesn't have a label param like this
+        )
+
+    # ------------------ Tab 3: Quiz Generator ------------------
+    with gr.Tab("📝 Quiz Generator") as tab3:
+        # Scoreboard only in this tab with retro neon style
+        scoreboard = gr.Markdown("Score: 0 | Streak: 0", elem_id="quiz-scoreboard")
+        gr.Markdown("<h3 class='neon-text'>Generate Quiz from Transcript</h3>")
+        gr.Markdown("<p class='retro-panel neon-text'>Click <strong>Generate Quiz</strong> to start. Answer each question and review your score and correct answer streak after each question.</p>")
+        generate_btn = gr.Button("Generate Quiz", elem_classes="accent-bg")
+        quiz_question = gr.Markdown("Question will appear here", elem_classes="retro-panel neon-text")
+        with gr.Row():
+            option_button1 = gr.Button("Option 1", elem_classes="accent-bg")
+            option_button2 = gr.Button("Option 2", elem_classes="accent-bg")
+            option_button3 = gr.Button("Option 3", elem_classes="accent-bg")
+            option_button4 = gr.Button("Option 4", elem_classes="accent-bg")
+        feedback_box = gr.Textbox(label="Feedback", interactive=False, elem_classes="retro-panel neon-text")
+        next_btn = gr.Button("Next Question", elem_classes="accent-bg")
+
+    # ------------------ Button Callback Bindings (Placeholder - Add your actual functions) ------------------
+
+    transcribe_button.click(
+        fn=handle_transcription_request,
+        inputs=[audio_input],
+        outputs=[latest_chunk_textbox, full_transcript_textbox, audio_input, status_textbox, transcription_input_status_textbox]
+    )
+    index_button.click(
+        fn=handle_indexing_request,
+        inputs=[full_transcript_textbox],
+        outputs=[indexing_status_display]
+    )
+    clear_button.click(
+        fn=clear_transcript_data,
+        inputs=None,
+        outputs=[full_transcript_textbox, status_textbox]
+    )
+    ask_button.click(
+        fn=handle_query_request,
+        inputs=[query_input_textbox],
+        outputs=[answer_display]
+    )
+    generate_btn.click(
+        fn=generate_quiz_and_buttons,
+        inputs=[full_transcript_textbox],
+        outputs=[quiz_question, option_button1, option_button2, option_button3, option_button4, feedback_box, scoreboard]
+    )
+    option_button1.click(
+        fn=lambda: select_answer_and_update(0),
+        inputs=[],
+        outputs=[quiz_question, option_button1, option_button2, option_button3, option_button4, feedback_box, scoreboard]
+    )
+    option_button2.click(
+        fn=lambda: select_answer_and_update(1),
+        inputs=[],
+        outputs=[quiz_question, option_button1, option_button2, option_button3, option_button4, feedback_box, scoreboard]
+    )
+    option_button3.click(
+        fn=lambda: select_answer_and_update(2),
+        inputs=[],
+        outputs=[quiz_question, option_button1, option_button2, option_button3, option_button4, feedback_box, scoreboard]
+    )
+    option_button4.click(
+        fn=lambda: select_answer_and_update(3),
+        inputs=[],
+        outputs=[quiz_question, option_button1, option_button2, option_button3, option_button4, feedback_box, scoreboard]
+    )
+    next_btn.click(
+        fn=advance_to_next_question,
+        inputs=[],
+        outputs=[quiz_question, option_button1, option_button2, option_button3, option_button4, feedback_box, scoreboard]
+    )
+
+app.launch()