Update app.py

app.py

@@ -1,61 +1,45 @@
 import gradio as gr
-import numpy as np
-import cv2
-from tensorflow.keras.models import load_model
-from gtts import gTTS
-import tempfile
 from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
 
-# Load chatbot model
-from transformers import pipeline
-
-chatbot = pipeline("conversational", model="facebook/blenderbot_small-90M")
-
-
-# Load emotion model
-model = load_model("emotion_model.h5")
-emotion_labels = ['Angry','Disgust','Fear','Happy','Sad','Surprise','Neutral']
-
-def detect_emotion(img):
-    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-    resized = cv2.resize(gray, (48, 48)).reshape(1, 48, 48, 1) / 255.0
-    prediction = model.predict(resized)
-    return emotion_labels[np.argmax(prediction)]
-
-def get_chatbot_reply(user_message):
-   from transformers import Conversation
-
-def get_chatbot_reply(user_message):
-    conversation = Conversation(user_message)
-    result = chatbot(conversation)
-    return result.generated_responses[-1]
-
-def smart_mirror(image, user_input):
-    emotion = detect_emotion(image)
-
-    if emotion in ["Sad", "Angry", "Fear"]:
-        if not user_input.strip():
-            reply = f"You seem {emotion.lower()}. Want to talk about it?"
-        else:
-            reply = get_chatbot_reply(user_input)
-    else:
-        reply = f"You look {emotion.lower()}! Keep shining ☀️"
-
-    tts = gTTS(text=reply)
-    audio_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
-    tts.save(audio_file.name)
-
-    return f"Emotion: {emotion}\nResponse: {reply}", audio_file.name
-
-interface = gr.Interface(
-    fn=smart_mirror,
-    inputs=[
-        gr.Image(type="numpy", label="Upload or Capture Image"),
-        gr.Textbox(lines=2, placeholder="Say something...", label="Your Message (optional)")
-    ],
-    outputs=["text", "audio"],
-    title="SmartMirror Counseling AI",
-    description="An AI Smart Mirror that detects your mood and supports you like a real friend."
-)
-
-interface.launch()
+# Load a lightweight model to fit in Spaces memory
+model_name = "facebook/blenderbot_small-90M"
+tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
+model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
+
+# Conversation history
+chat_history = ""
+
+def chatbot_response(user_message):
+    global chat_history
+    counseling_prefix = (
+        "You are a friendly counselor and caring friend. "
+        "When the user is sad, comfort them with empathy and motivational quotes or jokes. "
+        "When the user is happy, encourage and celebrate with them.\n"
+    )
+
+    # Append to conversation
+    full_input = counseling_prefix + chat_history + f"User: {user_message}\nAI:"
+    inputs = tokenizer([full_input], return_tensors="pt")
+    reply_ids = model.generate(**inputs, max_length=200, pad_token_id=tokenizer.eos_token_id)
+    reply = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
+
+    # Save conversation
+    chat_history += f"User: {user_message}\nAI: {reply}\n"
+    return reply
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align:center;'>🤖 Counseling Chatbot</h1><p style='text-align:center;'>Your caring AI friend</p>")
+    chatbot_ui = gr.Chatbot()
+    user_input = gr.Textbox(placeholder="Type your message here...", label="Your message")
+
+    def respond(message, history):
+        bot_reply = chatbot_response(message)
+        history.append((message, bot_reply))
+        return history, ""
+
+    user_input.submit(respond, [user_input, chatbot_ui], [chatbot_ui, user_input])
+
+# Launch app
+if __name__ == "__main__":
+    demo.launch()