Update app.py

app.py

@@ -1,10 +1,17 @@
-### 1. Imports and class names setup ###
 import gradio as gr
 import os
 import torch
 from model import create_effnetb2_model
 from timeit import default_timer as timer
 from typing import Tuple, Dict
+import pkg_resources
+
+# Check Gradio version
+try:
+    gradio_version = pkg_resources.get_distribution("gradio").version
+    print(f"Using Gradio version: {gradio_version}")
+except pkg_resources.DistributionNotFound:
+    raise ImportError("Gradio is not installed. Please install it using 'pip install gradio'.")
 
 # Load class names
 try:
@@ -13,7 +20,7 @@ try:
 except FileNotFoundError:
     raise FileNotFoundError("class_names.txt not found.")
 
-### 2. Model and transforms preparation ###
+# Model and transforms preparation
 try:
     effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101)
 except Exception as e:
@@ -32,7 +39,7 @@ except FileNotFoundError:
 except Exception as e:
     raise Exception(f"Error loading weights: {str(e)}")
 
-### 3. Predict function ###
+# Predict function
 def predict(img) -> Tuple[Dict, float]:
     try:
         start_time = timer()
@@ -48,7 +55,7 @@ def predict(img) -> Tuple[Dict, float]:
     except Exception as e:
         return {"error": f"Prediction failed: {str(e)}"}, 0.0
 
-### 4. Gradio app ###
+# Gradio app
 title = "FoodVision 101 🍔👁"
 description = "An EfficientNetB2 feature extractor to classify 101 food classes."
 
@@ -58,6 +65,7 @@ except FileNotFoundError:
     example_list = []
     print("Warning: 'examples/' directory not found.")
 
+# Simplified Gradio interface
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
@@ -70,5 +78,8 @@ demo = gr.Interface(
     description=description,
 )
 
-# Launch without share=True for Hugging Face Spaces
-demo.launch()
+# Launch with share=True for Hugging Face Spaces
+try:
+    demo.launch(share=True)
+except Exception as e:
+    raise Exception(f"Failed to launch Gradio app: {str(e)}")