app.py

import gradio as gr
import torch
import speech_recognition as sr
from groq import Groq
import os
import tempfile
from gtts import gTTS
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set device (CPU only for Hugging Face Spaces free tier)
device = torch.device("cpu")
logger.info(f"Using device: {device}")

# Groq API client with API key from Hugging Face Secrets
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
if not GROQ_API_KEY:
    logger.error("GROQ_API_KEY environment variable not set")
    raise ValueError("GROQ_API_KEY environment variable not set")

try:
    client = Groq(api_key=GROQ_API_KEY)
    logger.info("Grok client initialized successfully")
except Exception as e:
    logger.error(f"Error initializing Groq client: {str(e)}")
    raise

# Functions
def predict_text_emotion(text):
    prompt = f"The user has entered text '{text}' classify user's emotion as happy or sad or anxious or angry. Respond in only one word."
    try:
        completion = client.chat.completions.create(
            model="llama3-70b-8192",
            messages=[{"role": "user", "content": prompt}],
            temperature=1,
            max_tokens=64,
            top_p=1,
            stream=False,
        )
        return completion.choices[0].message.content.strip().lower()
    except Exception as e:
        logger.error(f"Error with Groq API (text emotion): {str(e)}")
        return "neutral"

def transcribe_audio(audio_path):
    r = sr.Recognizer()
    try:
        with sr.AudioFile(audio_path) as source:
            audio_text = r.listen(source)
        text = r.recognize_google(audio_text)
        return text
    except sr.UnknownValueError:
        return "I didn’t catch that—could you try again?"
    except sr.RequestError as e:
        logger.error(f"Speech recognition error: {str(e)}")
        return "Speech recognition unavailable—try typing instead."
    except Exception as e:
        logger.error(f"Unexpected error in audio transcription: {str(e)}")
        return "Error processing audio."

def generate_response(user_input, emotion):
    prompt = f"The user is feeling {emotion}. They said: '{user_input}'. Respond in a friendly caring manner with the user so the user feels being loved."
    try:
        completion = client.chat.completions.create(
            model="llama3-70b-8192",
            messages=[{"role": "user", "content": prompt}],
            temperature=1,
            max_tokens=64,
            top_p=1,
            stream=False,
        )
        return completion.choices[0].message.content
    except Exception as e:
        logger.error(f"Error with Groq API (response generation): {str(e)}")
        return "I'm here for you, but something went wrong. How can I help?"

def text_to_speech(text):
    try:
        tts = gTTS(text=text, lang='en', slow=False)
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
            tts.save(temp_audio.name)
            return temp_audio.name
    except Exception as e:
        logger.error(f"Error generating speech: {str(e)}")
        return None

# Chat function for Gradio with voice output
def chat_function(input_type, text_input, audio_input, chat_history):
    if input_type == "text" and text_input:
        user_input = text_input
    elif input_type == "voice" and audio_input:
        user_input = transcribe_audio(audio_input)
    else:
        return chat_history, "Please provide text or voice input.", gr.update(value=text_input), None

    emotion = predict_text_emotion(user_input)
    response = generate_response(user_input, emotion)
    
    chat_history = chat_history or []
    chat_history.append({"role": "user", "content": user_input})
    chat_history.append({"role": "assistant", "content": response})

    audio_output = text_to_speech(response)
    return chat_history, f"Detected Emotion: {emotion}", "", audio_output

# Custom CSS for styling
css = """
.chatbot .message-user {
    background-color: #e3f2fd;
    border-radius: 10px;
    padding: 10px;
    margin: 5px 0;
}
.chatbot .message-assistant {
    background-color: #c8e6c9;
    border-radius: 10px;
    padding: 10px;
    margin: 5px 0;
}
.input-container {
    padding: 10px;
    background-color: #f9f9f9;
    border-radius: 10px;
    margin-top: 10px;
}
"""

# Build the Gradio interface
with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
    gr.Markdown(
        """
        # Multimodal Mental Health AI Agent
        Chat with our empathetic AI designed to support you by understanding your emotions through text and voice.
        """
    )

    with gr.Row():
        with gr.Column(scale=1):
            emotion_display = gr.Textbox(label="Emotion", interactive=False, placeholder="Detected emotion will appear here")
        
        with gr.Column(scale=3):
            chatbot = gr.Chatbot(label="Conversation History", height=500, type="messages", elem_classes="chatbot")

    with gr.Row(elem_classes="input-container"):
        input_type = gr.Radio(["text", "voice"], label="Input Method", value="text")
        text_input = gr.Textbox(label="Type Your Message", placeholder="How are you feeling today?", visible=True)
        audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Your Message", visible=False)
        submit_btn = gr.Button("Send", variant="primary")
        clear_btn = gr.Button("Clear Chat", variant="secondary")
        audio_output = gr.Audio(label="Assistant Response", type="filepath", interactive=False, autoplay=True)

    # Dynamic visibility based on input type
    def update_visibility(input_type):
        return gr.update(visible=input_type == "text"), gr.update(visible=input_type == "voice")

    input_type.change(fn=update_visibility, inputs=input_type, outputs=[text_input, audio_input])

    # Submit action with voice output
    submit_btn.click(
        fn=chat_function,
        inputs=[input_type, text_input, audio_input, chatbot],
        outputs=[chatbot, emotion_display, text_input, audio_output]
    )

    # Clear chat and audio
    clear_btn.click(
        lambda: ([], "", "", None),
        inputs=None,
        outputs=[chatbot, emotion_display, text_input, audio_output]
    )

# Launch the app (commented out for Hugging Face Spaces)
# if __name__ == "__main__":
#     app.launch(server_name="0.0.0.0", server_port=7860)