Update app.py

app.py

@@ -1,6 +1,5 @@
 import os
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Disable GPU warnings
-
 import gradio as gr
 import tensorflow as tf
 import numpy as np
@@ -11,14 +10,25 @@ model = tf.keras.models.load_model("MobileNet_model.h5")
 class_names = ["Fake", "Low", "Medium", "High"]
 
 def predict_image(img):
-    img = img.resize((128, 128))
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-    predictions = model.predict(img_array)
-    class_index = np.argmax(predictions, axis=1)[0]
-    confidence_scores = {class_names[i]: float(predictions[0][i]) for i in range(len(class_names))}
-    return {"Predicted Class": class_names[class_index], "Confidence Scores": confidence_scores}
+    if img is None:
+        return {"error": "No image provided"}
+    try:
+        img = img.resize((128, 128))
+        img_array = np.array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+        predictions = model.predict(img_array)
+        class_index = np.argmax(predictions, axis=1)[0]
+        confidence_scores = {class_names[i]: float(predictions[0][i]) for i in range(len(class_names))}
+        return {"Predicted Class": class_names[class_index], "Confidence Scores": confidence_scores}
+    except Exception as e:
+        return {"error": str(e)}
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="json"
+)
 
-iface = gr.Interface(fn=predict_image, inputs="image", outputs="json")
-iface.launch(server_name="0.0.0.0", server_port=7860)  # Fix for Hugging Face
+# Add show_error=True to enable verbose error reporting
+iface.launch(server_name="0.0.0.0", server_port=7860, show_error=True)  # Fix for Hugging Face