app.py

import streamlit as st
import time
import requests
from streamlit.components.v1 import html
import os

# Import transformers and cache the help agent for performance
@st.cache_resource
def get_help_agent():
    from transformers import pipeline
    # Using BlenderBot 400M Distill as the public conversational model (used elsewhere)
    return pipeline("conversational", model="facebook/blenderbot-400M-distill")

# Custom CSS for professional look (fixed text color) with speech recognition
def inject_custom_css():
    st.markdown("""
    <style>
        @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;600;700&display=swap');

        * {
            font-family: 'Poppins', sans-serif;
        }

        .title {
            font-size: 3rem !important;
            font-weight: 700 !important;
            color: #6C63FF !important;
            text-align: center;
            margin-bottom: 0.5rem;
        }

        .subtitle {
            font-size: 1.2rem !important;
            text-align: center;
            color: #666 !important;
            margin-bottom: 2rem;
        }

        .question-box {
            background: #F8F9FA;
            border-radius: 15px;
            padding: 2rem;
            margin: 1.5rem 0;
            box-shadow: 0 4px 6px rgba(0,0,0,0.1);
            color: black !important;
        }

        .answer-btn {
            border-radius: 12px !important;
            padding: 0.5rem 1.5rem !important;
            font-weight: 600 !important;
            margin: 0.5rem !important;
        }

        .yes-btn {
            background: #6C63FF !important;
            color: white !important;
        }

        .no-btn {
            background: #FF6B6B !important;
            color: white !important;
        }

        .final-reveal {
            animation: fadeIn 2s;
            font-size: 2.5rem;
            color: #6C63FF;
            text-align: center;
            margin: 2rem 0;
        }

        @keyframes fadeIn {
            from { opacity: 0; }
            to { opacity: 1; }
        }

        .confetti {
            position: fixed;
            top: 0;
            left: 0;
            width: 100%;
            height: 100%;
            pointer-events: none;
            z-index: 1000;
        }
        
        .confidence-meter {
            height: 10px;
            background: linear-gradient(90deg, #FF6B6B 0%, #6C63FF 100%);
            border-radius: 5px;
            margin: 10px 0;
        }
        
        .mic-btn {
            margin-top: 29px;
            border: none;
            background: none;
            cursor: pointer;
            font-size: 1.5em;
            padding: 0;
        }
    </style>
    <script>
    function startSpeechRecognition(inputId) {
        const recognition = new (window.SpeechRecognition || window.webkitSpeechRecognition)();
        recognition.lang = 'en-US';
        recognition.interimResults = false;
        recognition.maxAlternatives = 1;

        recognition.onresult = function(event) {
            const transcript = event.results[0][0].transcript.toLowerCase();
            const inputElement = document.getElementById(inputId);
            if (inputElement) {
                inputElement.value = transcript;
                // Trigger Streamlit's input change detection
                const event = new Event('input', { bubbles: true });
                inputElement.dispatchEvent(event);
            }
        };

        recognition.onerror = function(event) {
            console.error('Speech recognition error', event.error);
        };

        recognition.start();
    }
    </script>
    """, unsafe_allow_html=True)

# Confetti animation
def show_confetti():
    html("""
    <canvas id="confetti-canvas" class="confetti"></canvas>
    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/confetti.browser.min.js"></script>
    <script>
    const canvas = document.getElementById('confetti-canvas');
    const confetti = confetti.create(canvas, { resize: true });
    confetti({
        particleCount: 150,
        spread: 70,
        origin: { y: 0.6 }
    });
    setTimeout(() => { canvas.remove(); }, 5000);
    </script>
    """)

# Enhanced AI question generation for guessing game using Llama model
def ask_llama(conversation_history, category, is_final_guess=False):
    api_url = "https://api.groq.com/openai/v1/chat/completions"
    headers = {
        "Authorization": "Bearer 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",
        "Content-Type": "application/json"
    }

    system_prompt = f"""You're playing 20 questions to guess a {category}. Follow these rules:
1. Ask strategic, non-repeating yes/no questions that narrow down possibilities
2. Consider all previous answers carefully before asking next question
3. If you're very confident (80%+ sure), respond with "Final Guess: [your guess]"
4. For places: ask about continent, climate, famous landmarks, country, city or population
5. For people: ask about fictional or real, profession, gender, alive/dead, nationality, or fame
6. For objects: ask about size, color, usage, material, or where it's found
7. Never repeat questions and always make progress toward guessing"""

    if is_final_guess:
        prompt = f"""Based on these answers about a {category}, provide ONLY your final guess with no extra text:
{conversation_history}"""
    else:
        prompt = "Ask your next strategic yes/no question that will best narrow down the possibilities."

    messages = [
        {"role": "system", "content": system_prompt},
        *conversation_history,
        {"role": "user", "content": prompt}
    ]

    data = {
        "model": "llama-3.3-70b-versatile",
        "messages": messages,
        "temperature": 0.7 if is_final_guess else 0.8,
        "max_tokens": 100
    }

    try:
        response = requests.post(api_url, headers=headers, json=data)
        response.raise_for_status()
        return response.json()["choices"][0]["message"]["content"]
    except Exception as e:
        st.error(f"Error calling Llama API: {str(e)}")
        return "Could not generate question"

# New function for the help AI assistant using the Hugging Face InferenceClient
def ask_help_agent(query):
    try:
        from huggingface_hub import InferenceClient
        # Initialize the client with the provided model
        client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=os.environ.get("HF_HUB_TOKEN"))
        system_message = "You are a friendly Chatbot."
        
        # Build history from session state (if any)
        history = []
        if "help_conversation" in st.session_state:
            for msg in st.session_state.help_conversation:
                # Each history entry is a tuple: (user query, assistant response)
                history.append((msg.get("query", ""), msg.get("response", "")))
        
        messages = [{"role": "system", "content": system_message}]
        for user_msg, bot_msg in history:
            if user_msg:
                messages.append({"role": "user", "content": user_msg})
            if bot_msg:
                messages.append({"role": "assistant", "content": bot_msg})
        messages.append({"role": "user", "content": query})
        
        response_text = ""
        # Using streaming to collect the entire response from the model
        for message in client.chat_completion(
            messages,
            max_tokens=150,
            stream=True,
            temperature=0.7,
            top_p=0.95,
        ):
            token = message.choices[0].delta.content
            response_text += token
        return response_text
    except Exception as e:
        return f"Error in help agent: {str(e)}"

# Main game logic
def main():
    inject_custom_css()

    st.markdown('<div class="title">KASOTI</div>', unsafe_allow_html=True)
    st.markdown('<div class="subtitle">The Smart Guessing Game</div>', unsafe_allow_html=True)

    if 'game_state' not in st.session_state:
        st.session_state.game_state = "start"
        st.session_state.questions = []
        st.session_state.current_q = 0
        st.session_state.answers = []
        st.session_state.conversation_history = []
        st.session_state.category = None
        st.session_state.final_guess = None
        st.session_state.help_conversation = []  # separate history for help agent

    # Start screen
    if st.session_state.game_state == "start":
        st.markdown("""
        <div class="question-box">
            <h3>Welcome to <span style='color:#6C63FF;'>KASOTI 🎯</span></h3>
            <p>Think of something and I'll try to guess it in 20 questions or less!</p>
            <p>Choose a category:</p>
            <ul>
                <li><strong>Person</strong> - celebrity, fictional character, historical figure</li>
                <li><strong>Place</strong> - city, country, landmark, geographical location</li>
                <li><strong>Object</strong> - everyday item, tool, vehicle, etc.</li>
            </ul>
            <p>Type or speak your category below to begin:</p>
        </div>
        """, unsafe_allow_html=True)

        with st.form("start_form"):
            # Create columns for input and microphone button
            col1, col2 = st.columns([4, 1])
            with col1:
                category_input = st.text_input(
                    "Enter category (person/place/object):",
                    key="category_input"
                ).strip().lower()
            
            with col2:
                st.markdown(
                    """
                    <button type="button" onclick="startSpeechRecognition('text_input-category_input')" 
                            class="mic-btn">
                        🎤
                    </button>
                    """,
                    unsafe_allow_html=True
                )
            
            if st.form_submit_button("Start Game"):
                if not category_input:
                    st.error("Please enter a category!")
                elif category_input not in ["person", "place", "object"]:
                    st.error("Please enter either 'person', 'place', or 'object'!")
                else:
                    st.session_state.category = category_input
                    first_question = ask_llama([
                        {"role": "user", "content": "Ask your first strategic yes/no question."}
                    ], category_input)
                    st.session_state.questions = [first_question]
                    st.session_state.conversation_history = [
                        {"role": "assistant", "content": first_question}
                    ]
                    st.session_state.game_state = "gameplay"
                    st.experimental_rerun()

    # Gameplay screen
    elif st.session_state.game_state == "gameplay":
        current_question = st.session_state.questions[st.session_state.current_q]
        
        # Check if AI made a guess
        if "Final Guess:" in current_question:
            st.session_state.final_guess = current_question.split("Final Guess:")[1].strip()
            st.session_state.game_state = "confirm_guess"
            st.experimental_rerun()
        
        st.markdown(f'<div class="question-box">Question {st.session_state.current_q + 1}/20:<br><br>'
                    f'<strong>{current_question}</strong></div>',
                    unsafe_allow_html=True)
        
        with st.form("answer_form"):
            answer_key = f"answer_{st.session_state.current_q}"
            
            # Create columns for input and microphone button
            col1, col2 = st.columns([4, 1])
            with col1:
                answer_input = st.text_input(
                    "Your answer (yes/no/both) - speak or type:",
                    key=answer_key
                ).strip().lower()
            
            with col2:
                st.markdown(
                    f"""
                    <button type="button" onclick="startSpeechRecognition('text_input-{answer_key}')" 
                            class="mic-btn">
                        🎤
                    </button>
                    """,
                    unsafe_allow_html=True
                )
            
            if st.form_submit_button("Submit"):
                if answer_input not in ["yes", "no", "both"]:
                    st.error("Please answer with 'yes', 'no', or 'both'!")
                else:
                    st.session_state.answers.append(answer_input)
                    st.session_state.conversation_history.append(
                        {"role": "user", "content": answer_input}
                    )

                    # Generate next response
                    next_response = ask_llama(
                        st.session_state.conversation_history,
                        st.session_state.category
                    )
                    
                    # Check if AI made a guess
                    if "Final Guess:" in next_response:
                        st.session_state.final_guess = next_response.split("Final Guess:")[1].strip()
                        st.session_state.game_state = "confirm_guess"
                    else:
                        st.session_state.questions.append(next_response)
                        st.session_state.conversation_history.append(
                            {"role": "assistant", "content": next_response}
                        )
                        st.session_state.current_q += 1

                        # Stop after 20 questions max
                        if st.session_state.current_q >= 20:
                            st.session_state.game_state = "result"
                    
                    st.experimental_rerun()

        # Side Help Option: independent chat with an AI help assistant using Hugging Face model
        with st.expander("Need Help? Chat with AI Assistant"):
            help_query = st.text_input("Enter your help query:", key="help_query")
            if st.button("Send", key="send_help"):
                if help_query:
                    help_response = ask_help_agent(help_query)
                    st.session_state.help_conversation.append({"query": help_query, "response": help_response})
                else:
                    st.error("Please enter a query!")
            if st.session_state.help_conversation:
                for msg in st.session_state.help_conversation:
                    st.markdown(f"**You:** {msg['query']}")
                    st.markdown(f"**Help Assistant:** {msg['response']}")

    # Guess confirmation screen using text input response
    elif st.session_state.game_state == "confirm_guess":
        st.markdown(f'<div class="question-box">🤖 My Final Guess:<br><br>'
                    f'<strong>Is it {st.session_state.final_guess}?</strong></div>',
                    unsafe_allow_html=True)
        
        with st.form("confirm_form"):
            # Create columns for input and microphone button
            col1, col2 = st.columns([4, 1])
            with col1:
                confirm_input = st.text_input(
                    "Type or speak your answer (yes/no/both):",
                    key="confirm_input"
                ).strip().lower()
            
            with col2:
                st.markdown(
                    """
                    <button type="button" onclick="startSpeechRecognition('text_input-confirm_input')" 
                            class="mic-btn">
                        🎤
                    </button>
                    """,
                    unsafe_allow_html=True
                )
            
            if st.form_submit_button("Submit"):
                if confirm_input not in ["yes", "no", "both"]:
                    st.error("Please answer with 'yes', 'no', or 'both'!")
                else:
                    if confirm_input == "yes":
                        st.session_state.game_state = "result"
                        st.experimental_rerun()
                        st.stop()  # Immediately halt further execution
                    else:
                        # Add negative response to history and continue gameplay
                        st.session_state.conversation_history.append(
                            {"role": "user", "content": "no"}
                        )
                        st.session_state.game_state = "gameplay"
                        next_response = ask_llama(
                            st.session_state.conversation_history,
                            st.session_state.category
                        )
                        st.session_state.questions.append(next_response)
                        st.session_state.conversation_history.append(
                            {"role": "assistant", "content": next_response}
                        )
                        st.session_state.current_q += 1
                        st.experimental_rerun()

    # Result screen
    elif st.session_state.game_state == "result":
        if not st.session_state.final_guess:
            # Generate final guess if not already made
            qa_history = "\n".join(
                [f"Q{i+1}: {q}\nA: {a}" 
                 for i, (q, a) in enumerate(zip(st.session_state.questions, st.session_state.answers))]
            )
            
            final_guess = ask_llama(
                [{"role": "user", "content": qa_history}],
                st.session_state.category,
                is_final_guess=True
            )
            st.session_state.final_guess = final_guess.split("Final Guess:")[-1].strip()

        show_confetti()
        st.markdown(f'<div class="final-reveal">🎉 It\'s...</div>', unsafe_allow_html=True)
        time.sleep(1)
        st.markdown(f'<div class="final-reveal" style="font-size:3.5rem;color:#6C63FF;">{st.session_state.final_guess}</div>',
                    unsafe_allow_html=True)
        st.markdown(f"<p style='text-align:center'>Guessed in {len(st.session_state.questions)} questions</p>", 
                    unsafe_allow_html=True)
        
        if st.button("Play Again", key="play_again"):
            st.session_state.clear()
            st.experimental_rerun()

if __name__ == "__main__":
    main()