app.py

import os
import re
import requests
import torch
import streamlit as st
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from transformers import pipeline
from langdetect import detect  # Ensure this package is installed

# ✅ Check for GPU or Default to CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"✅ Using device: {device}")  # Debugging info

# ✅ Environment Variables
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN is None:
    raise ValueError("HF_TOKEN is not set. Please add it to your environment variables.")

NASA_API_KEY = os.getenv("NASA_API_KEY")
if NASA_API_KEY is None:
    raise ValueError("NASA_API_KEY is not set. Please add it to your environment variables.")

# ✅ Set Up Streamlit
st.set_page_config(page_title="HAL - NASA ChatBot", page_icon="🚀")

# ✅ Initialize Session State Variables (Ensuring Chat History Persists)
if "chat_history" not in st.session_state:
    st.session_state.chat_history = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
if "listening" not in st.session_state:
    st.session_state.listening = False

# ✅ Initialize Hugging Face Model (Explicitly Set to CPU/GPU)
def get_llm_hf_inference(model_id="meta-llama/Llama-2-7b-chat-hf", max_new_tokens=800, temperature=0.3):
    return HuggingFaceEndpoint(
        repo_id=model_id,
        max_new_tokens=max_new_tokens,
        temperature=temperature,  # 🔥 Lowered temperature for more factual and structured responses
        token=HF_TOKEN,
        task="text-generation",
        device=-1 if device == "cpu" else 0  # ✅ Force CPU (-1) or GPU (0)
    )

# ✅ Ensure English Responses
def ensure_english(text):
    try:
        detected_lang = detect(text)
        if detected_lang != "en":
            return "⚠️ Sorry, I only respond in English. Can you rephrase your question?"
    except:
        return "⚠️ Language detection failed. Please ask your question again."
    return text

# ✅ Main Response Function (Fixing Repetition & Context)
def get_response(system_message, chat_history, user_text, max_new_tokens=800):
    # ✅ Ensure conversation history is included correctly
    filtered_history = "\n".join(
        f"{msg['role'].capitalize()}: {msg['content']}"
        for msg in chat_history[-5:]  # ✅ Only keep the last 5 exchanges to prevent overflow
    )
 
    prompt = PromptTemplate.from_template(
        "[INST] You are a highly knowledgeable AI assistant. Answer concisely, avoid repetition, and structure responses well."
        "\n\nCONTEXT:\n{chat_history}\n"
        "\nLATEST USER INPUT:\nUser: {user_text}\n"
        "\n[END CONTEXT]\n"
        "Assistant:"
    )

    # ✅ Invoke Hugging Face Model
    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.3)  # 🔥 Lowered temperature
    chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')

    response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=filtered_history))
    
    # Clean up the response - remove any "HAL:" prefix if present
    response = response.split("HAL:")[-1].strip() if "HAL:" in response else response.strip()
    response = ensure_english(response)

    if not response:
        response = "I'm sorry, but I couldn't generate a response. Can you rephrase your question?"

    # ✅ Update conversation history
    chat_history.append({'role': 'user', 'content': user_text})
    chat_history.append({'role': 'assistant', 'content': response})

    # ✅ Keep only last 10 exchanges to prevent unnecessary repetition
    return response, chat_history[-10:]

# ✅ Streamlit UI
st.title("🚀 HAL - NASA AI Assistant")

# ✅ Add styles and speech recognition JavaScript
st.markdown("""
    <style>
    .user-msg, .assistant-msg {
        padding: 11px;
        border-radius: 10px;
        margin-bottom: 5px;
        width: fit-content;
        max-width: 80%;
        text-align: justify;
    }
    .user-msg { background-color: #696969; color: white; }
    .assistant-msg { background-color: #333333; color: white; }
    .container { display: flex; flex-direction: column; align-items: flex-start; }
    .speech-button {
        background-color: #4CAF50;
        border: none;
        color: white;
        padding: 10px 15px;
        text-align: center;
        text-decoration: none;
        display: inline-block;
        font-size: 16px;
        margin: 4px 2px;
        cursor: pointer;
        border-radius: 12px;
    }
    .speak-button {
        background-color: #2196F3;
        border: none;
        color: white;
        padding: 5px 10px;
        text-align: center;
        text-decoration: none;
        display: inline-block;
        font-size: 12px;
        margin: 2px 2px;
        cursor: pointer;
        border-radius: 12px;
    }
    @media (max-width: 600px) { .user-msg, .assistant-msg { font-size: 16px; max-width: 100%; } }
    </style>
    
    <script>
    // Speech Recognition Setup
    let recognition;
    let isListening = false;
    
    function setupSpeechRecognition() {
        try {
            window.SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
            recognition = new SpeechRecognition();
            recognition.lang = 'en-US';
            recognition.interimResults = false;
            recognition.maxAlternatives = 1;
            
            recognition.onresult = function(event) {
                const speechResult = event.results[0][0].transcript;
                document.getElementById('speech-result').value = speechResult;
                document.getElementById('submit-speech').click();
            };
            
            recognition.onerror = function(event) {
                console.error('Speech recognition error:', event.error);
                isListening = false;
                updateMicButton();
            };
            
            recognition.onend = function() {
                isListening = false;
                updateMicButton();
            };
            
            return true;
        } catch (error) {
            console.error('Speech recognition not supported:', error);
            return false;
        }
    }
    
    function toggleSpeechRecognition() {
        if (!recognition) {
            if (!setupSpeechRecognition()) {
                alert('Speech recognition is not supported in your browser.');
                return;
            }
        }
        
        if (isListening) {
            recognition.stop();
            isListening = false;
        } else {
            recognition.start();
            isListening = true;
        }
        
        updateMicButton();
    }
    
    function updateMicButton() {
        const micButton = document.getElementById('mic-button');
        if (micButton) {
            micButton.textContent = isListening ? '🛑 Stop Listening' : '🎤 Start Voice Input';
            micButton.style.backgroundColor = isListening ? '#f44336' : '#4CAF50';
        }
    }
    
    // Text-to-Speech functionality
    function speakText(text) {
        const utterance = new SpeechSynthesisUtterance(text);
        utterance.lang = 'en-US';
        utterance.pitch = 1;
        utterance.rate = 1;
        window.speechSynthesis.speak(utterance);
    }
    
    // Initialize after the page loads
    document.addEventListener('DOMContentLoaded', function() {
        setupSpeechRecognition();
    });
    </script>
""", unsafe_allow_html=True)

# Add voice control components
col1, col2 = st.columns([4, 1])
with col1:
    user_input = st.chat_input("Type your message here...")

with col2:
    st.markdown("""
        <button id="mic-button" onclick="toggleSpeechRecognition()" class="speech-button">
            🎤 Start Voice Input
        </button>
        <input type="hidden" id="speech-result">
        <button id="submit-speech" style="display:none;"></button>
    """, unsafe_allow_html=True)

# Handle form for speech input (hidden)
speech_result = st.text_input("Speech Result", key="speech_input", label_visibility="collapsed")
if speech_result:
    user_input = speech_result
    # Reset the speech input
    st.session_state.speech_input = ""

if user_input:
    # Get response and update chat history
    response, st.session_state.chat_history = get_response(
        system_message="You are a helpful AI assistant specializing in NASA and space information.",
        user_text=user_input,
        chat_history=st.session_state.chat_history
    )

# ✅ Display chat history with speak buttons
st.markdown("<div class='container'>", unsafe_allow_html=True)
for i, message in enumerate(st.session_state.chat_history):
    if message["role"] == "user":
        st.markdown(f"<div class='user-msg'><strong>You:</strong> {message['content']}</div>", unsafe_allow_html=True)
    else:
        # Fix: Use JavaScript data attribute instead of trying to escape in f-string
        content_for_id = f"msg-{i}"
        st.markdown(
            f"""<div class='assistant-msg'>
                <strong>HAL:</strong> {message['content']}
                <button onclick="speakText(document.getElementById('{content_for_id}').textContent)" class="speak-button">
                    🔊 Speak
                </button>
                <span id="{content_for_id}" style="display:none">{message['content']}</span>
            </div>""", 
            unsafe_allow_html=True
        )
st.markdown("</div>", unsafe_allow_html=True)

# Add JavaScript event listener for the submit button
components_js = """
<script>
document.getElementById('submit-speech').addEventListener('click', function() {
    const speechResult = document.getElementById('speech-result').value;
    if (speechResult) {
        // Update the Streamlit text input with the speech result
        const textInputs = document.querySelectorAll('input[type="text"]');
        if (textInputs.length > 0) {
            const lastInput = textInputs[0];
            lastInput.value = speechResult;
            lastInput.dispatchEvent(new Event('input', { bubbles: true }));
            
            // Find and click the submit button
            setTimeout(() => {
                const buttons = document.querySelectorAll('button[kind="primaryForm"]');
                for (const button of buttons) {
                    if (button.textContent.includes('Submit')) {
                        button.click();
                        break;
                    }
                }
            }, 100);
        }
    }
});
</script>
"""
st.components.v1.html(components_js, height=0)