Update app.py

app.py

@@ -1,49 +1,57 @@
 import gradio as gr
-import openai, cv2, numpy as np
+import numpy as np
+import cv2
 from tensorflow.keras.models import load_model
 from gtts import gTTS
 import tempfile
+from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
 
-openai.api_key = "sk-REPLACE_WITH_YOUR_KEY"
+# Load chatbot model
+tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")
+chatbot = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B")
+
+# Load emotion model
 model = load_model("emotion_model.h5")
 emotion_labels = ['Angry','Disgust','Fear','Happy','Sad','Surprise','Neutral']
 
 def detect_emotion(img):
-    face = cv2.resize(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY), (48, 48)).reshape(1, 48, 48, 1)/255.0
-    preds = model.predict(face)
-    return emotion_labels[np.argmax(preds)]
-
-def gpt_counsel_response(emotion, user_input=""):
-    prompt = f"You are a friendly emotional counselor chatbot. A user is feeling {emotion.lower()}. "
-    if user_input:
-        prompt += f"They said: '{user_input}'. Respond with empathy and support."
-    else:
-        prompt += "Gently ask how they are feeling and offer motivation or comfort."
+    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)]
 
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "system", "content": "You are a compassionate AI counselor."},
-                  {"role": "user", "content": prompt}]
-    )
-    return response.choices[0].message.content.strip()
+def get_chatbot_reply(user_message):
+    inputs = tokenizer([user_message], return_tensors="pt")
+    reply_ids = chatbot.generate(**inputs)
+    reply = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
+    return reply
 
-def smart_mirror_ai(image, user_input):
+def smart_mirror(image, user_input):
     emotion = detect_emotion(image)
+
     if emotion in ["Sad", "Angry", "Fear"]:
-        reply = gpt_counsel_response(emotion, user_input)
+        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 reply, audio_file.name
 
-gr.Interface(
-    fn=smart_mirror_ai,
-    inputs=[gr.Image(type="numpy", label="Upload or Take Photo"),
-            gr.Textbox(label="Say something...")],
+    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 Counselor AI",
-    description="A GPT-powered mirror friend that supports you emotionally."
-).launch()
+    title="SmartMirror Counseling AI",
+    description="An AI Smart Mirror that detects your mood and supports you like a real friend."
+)
+
+interface.launch()