Update app.py

app.py

@@ -110,8 +110,8 @@ def predict_image(img, confidence_threshold):
             logits_3 = outputs_3.logits
             probabilities_3 = softmax(logits_3.cpu().numpy()[0])
         result_3 = {
-            labels_3[1]: float(probabilities_3[1])   # Real
-            labels_3[0]: float(probabilities_3[0]),  # AI
+            labels_3[1]: float(probabilities_3[1]),   # Real
+            labels_3[0]: float(probabilities_3[0])  # AI
         }
         print(result_3)
         # Ensure the result dictionary contains all class names
@@ -136,8 +136,8 @@ def predict_image(img, confidence_threshold):
             logits_4 = outputs_4.logits
             probabilities_4 = softmax(logits_4.cpu().numpy()[0])
         result_4 = {
-            labels_4[1]: float(probabilities_4[1])   # Real
-            labels_4[0]: float(probabilities_4[0]),  # AI
+            labels_4[1]: float(probabilities_4[1]),   # Real
+            labels_4[0]: float(probabilities_4[0])  # AI
         }
         print(result_4)
         # Ensure the result dictionary contains all class names
@@ -156,7 +156,12 @@ def predict_image(img, confidence_threshold):
     
     try:
         img_bytes = convert_pil_to_bytes(img_pil)
+
+        print(img_pill)
+        print(img_bytes)
+        
         response5_raw = call_inference(img_bytes)
+        print(response5_raw)
         response5 = response5_raw.json()
         print(response5)
         label_5 = f"Result: {response5}"
@@ -215,11 +220,11 @@ with gr.Blocks() as iface:
     gr.Markdown("# AI Generated Image Classification")
     
     with gr.Row():
-        with gr.Column():
+        with gr.Column(scale=2):
             image_input = gr.Image(label="Upload Image to Analyze", sources=['upload'], type='pil')
             confidence_slider = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Confidence Threshold")
             inputs = [image_input, confidence_slider]
-        with gr.Column():
+        with gr.Column(scale=3):
             image_output = gr.Image(label="Processed Image")
             # Custom HTML component to display results in 5 columns
             results_html = gr.HTML(label="Model Predictions")