Update app.py

app.py

@@ -1,30 +1,31 @@
 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
 import tempfile
+from gtts import gTTS
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
-# Set device
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
+# Set device (CPU only for Hugging Face Spaces free tier)
+device = torch.device("cpu")
+logger.info(f"Using device: {device}")
 
-# Clear GPU memory if using GPU
-if torch.cuda.is_available():
-    torch.cuda.empty_cache()
+# 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")
 
-# 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")
+    logger.info("Grok client initialized successfully")
 except Exception as e:
-    print(f"Error initializing Groq client: {str(e)}")
+    logger.error(f"Error initializing Groq client: {str(e)}")
     raise
 
 # Functions
@@ -35,72 +36,30 @@ def predict_text_emotion(text):
             model="llama3-70b-8192",
             messages=[{"role": "user", "content": prompt}],
             temperature=1,
-            max_completion_tokens=64,
+            max_tokens=64,
             top_p=1,
             stream=False,
-            stop=None,
         )
-        return completion.choices[0].message.content
+        return completion.choices[0].message.content.strip().lower()
     except Exception as e:
-        return f"Error with Grok API: {str(e)}"
+        logger.error(f"Error with Groq API (text emotion): {str(e)}")
+        return "neutral"
 
 def transcribe_audio(audio_path):
     r = sr.Recognizer()
-    with sr.AudioFile(audio_path) as source:
-        audio_text = r.listen(source)
     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:
+    except sr.RequestError as e:
+        logger.error(f"Speech recognition error: {str(e)}")
         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="llama3-70b-8192",
-            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"
+        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."
@@ -109,24 +68,23 @@ def generate_response(user_input, emotion):
             model="llama3-70b-8192",
             messages=[{"role": "user", "content": prompt}],
             temperature=1,
-            max_completion_tokens=64,
+            max_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)}"
+        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)
-        # Create a temporary file to store the audio
         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
             tts.save(temp_audio.name)
             return temp_audio.name
     except Exception as e:
-        print(f"Error generating speech: {str(e)}")
+        logger.error(f"Error generating speech: {str(e)}")
         return None
 
 # Chat function for Gradio with voice output
@@ -138,45 +96,36 @@ def chat_function(input_type, text_input, audio_input, chat_history):
     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)
+    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: {combined_emotion}", "", audio_output
+    return chat_history, f"Detected Emotion: {emotion}", "", audio_output
 
-# Custom CSS for better styling
+# Custom CSS for 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>
+.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
@@ -184,7 +133,7 @@ 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.
+        Chat with our empathetic AI designed to support you by understanding your emotions through text and voice.
         """
     )
 
@@ -198,7 +147,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
     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)
+        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)
@@ -223,6 +172,6 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
         outputs=[chatbot, emotion_display, text_input, audio_output]
     )
 
-# Launch the app (for local testing)
-if __name__ == "__main__":
-    app.launch(server_name="0.0.0.0", server_port=7860)
+# Launch the app (commented out for Hugging Face Spaces)
+# if __name__ == "__main__":
+#     app.launch(server_name="0.0.0.0", server_port=7860)