Update app.py

app.py

@@ -1,33 +1,34 @@
 import gradio as gr
 from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from tensorflow.keras.preprocessing.image import img_to_array
 import numpy as np
+from PIL import Image
 
-# Load model
-model = load_model("waste_classifier_model.h5")
+# 🔹 Load your saved model
+model = load_model("")
+
+# 🔹 Define your class labels (must match model training)
 class_names = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
 
-def predict_image(image):
-    # Preprocess
-    img = image.resize((224, 224))
-    img_array = img_to_array(img) / 255.0
+# 🔹 Prediction function
+def predict_from_camera(image):
+    image = image.resize((224, 224))  # Resize for model input
+    img_array = img_to_array(image) / 255.0  # Normalize
     img_array = np.expand_dims(img_array, axis=0)
-    
-    # Predict
-    predictions = model.predict(img_array)[0]
-    pred_index = np.argmax(predictions)
-    confidence = float(np.max(predictions))
-    label = class_names[pred_index]
-    
-    return {label: confidence}
 
-# Gradio Interface
-iface = gr.Interface(
-    fn=predict_image,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(num_top_classes=3),
-    title="Waste Classification with MobileNetV2",
-    description="Upload an image of waste (plastic, metal, paper, etc.), and this model will classify it."
+    prediction = model.predict(img_array)[0]
+    predicted_class = class_names[np.argmax(prediction)]
+    confidence = float(np.max(prediction))
+
+    return f"{predicted_class} ({confidence*100:.1f}%)"
+
+# 🔹 Gradio live webcam interface
+interface = gr.Interface(
+    fn=predict_from_camera,
+    inputs=gr.Image(source="webcam", streaming=True, type="pil"),
+    outputs="text",
+    title="Live Waste Classification",
+    description="Show waste to your webcam and the model will predict its type in real-time."
 )
 
-iface.launch()
+interface.launch()