app.py

import gradio as gr
import speech_recognition as sr
from groq import Groq
import os
import time
import base64
from io import BytesIO
from gtts import gTTS

# Set device


# Grok API client with API key (stored as environment variable for security)
GROQ_API_KEY = os.getenv("GROQ_API_KEY", "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")  # Replace with your key or use env variable
try:
    client = Groq(api_key=GROQ_API_KEY)
    print("Grok client initialized successfully")
except Exception as e:
    print(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="llama-3.2-90b-vision-preview",
            messages=[{"role": "user", "content": prompt}],
            temperature=1,
            max_completion_tokens=64,
            top_p=1,
            stream=False,
            stop=None,
        )
        return completion.choices[0].message.content
    except Exception as e:
        return f"Error with Grok API: {str(e)}"

def transcribe_audio(audio_path):
    r = sr.Recognizer()
    with sr.AudioFile(audio_path) as source:
        audio_text = r.listen(source)
    try:
        text = r.recognize_google(audio_text)
        return text
    except sr.UnknownValueError:
        return "I didn’t catch that—could you try again?"
    except sr.RequestError:
        return "Speech recognition unavailable—try typing instead."

def capture_webcam_frame():
    cap = cv2.VideoCapture(0)
    if not cap.isOpened():
        return None
    start_time = time.time()
    while time.time() - start_time < 2:
        ret, frame = cap.read()
        if ret:
            _, buffer = cv2.imencode('.jpg', frame)
            img_base64 = base64.b64encode(buffer).decode('utf-8')
            img_url = f"data:image/jpeg;base64,{img_base64}"
            cap.release()
            return img_url
    cap.release()
    return None

def detect_facial_emotion():
    img_url = capture_webcam_frame()
    if not img_url:
        return "neutral"
    try:
        completion = client.chat.completions.create(
            model="llama-3.2-90b-vision-preview",
            messages=[
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "Identify user's facial emotion into happy or sad or anxious or angry. Respond in one word only"},
                        {"type": "image_url", "image_url": {"url": img_url}}
                    ]
                }
            ],
            temperature=1,
            max_completion_tokens=20,
            top_p=1,
            stream=False,
            stop=None,
        )
        emotion = completion.choices[0].message.content.strip().lower()
        if emotion not in ["happy", "sad", "anxious", "angry"]:
            return "neutral"
        return emotion
    except Exception as e:
        print(f"Error with Grok facial detection: {str(e)}")
        return "neutral"

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="llama-3.2-90b-vision-preview",
            messages=[{"role": "user", "content": prompt}],
            temperature=1,
            max_completion_tokens=64,
            top_p=1,
            stream=False,
            stop=None,
        )
        return completion.choices[0].message.content
    except Exception as e:
        return f"Error with Groq API: {str(e)}"

def text_to_speech(text):
    try:
        tts = gTTS(text=text, lang='en', slow=False)
        audio_buffer = BytesIO()
        tts.write_to_fp(audio_buffer)
        audio_buffer.seek(0)
        return audio_buffer
    except Exception as e:
        print(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

    text_emotion = predict_text_emotion(user_input)
    if not chat_history:
        gr.Info("Please look at the camera for emotion detection...")
        facial_emotion = detect_facial_emotion()
    else:
        facial_emotion = "neutral"

    emotions = [e for e in [text_emotion, facial_emotion] if e and e != "neutral"]
    combined_emotion = emotions[0] if emotions else "neutral"

    response = generate_response(user_input, combined_emotion)
    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: {combined_emotion}", "", audio_output

# Custom CSS for better styling
css = """
<style>
    .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;
    }
</style>
"""

# 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 facial expressions.
        """
    )

    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(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 (for local testing; deployment will handle this differently)
if __name__ == "__main__":
    app.launch(server_name="0.0.0.0", server_port=7860)