Update app.py

app.py

@@ -1,227 +1,220 @@
-import gradio as gr
-import torch
-import cv2
-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
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# Clear GPU memory if using GPU
-if torch.cuda.is_available():
-    torch.cuda.empty_cache()
-
-# 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__":
+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)