Update app.py

app.py

@@ -33,21 +33,48 @@ def make_gradcam_heatmap(img_array, model):
     
     return heatmap.numpy()
 
-def predict(input_image):
-    # Handle both file upload and base64 string
-    if isinstance(input_image, str) and input_image.startswith('data:'):
-        # Base64 input
-        base64_data = input_image.split(',')[1]
-        image_bytes = base64.b64decode(base64_data)
-        img = Image.open(io.BytesIO(image_bytes)).convert('RGB')
-    elif isinstance(input_image, str):
-        # Plain base64 without prefix
-        image_bytes = base64.b64decode(input_image)
+def predict_from_base64(base64_string):
+    try:
+        # Remove data URL prefix if present
+        if ',' in base64_string:
+            base64_string = base64_string.split(',')[1]
+        
+        # Decode base64 to image
+        image_bytes = base64.b64decode(base64_string)
         img = Image.open(io.BytesIO(image_bytes)).convert('RGB')
-    else:
-        # Normal image array from Gradio
-        img = Image.fromarray(input_image).convert('RGB')
+        
+        img = img.resize((224, 224))
+        img_array = np.array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+        
+        predictions = model.predict(img_array)
+        pred_class = CLASS_NAMES[np.argmax(predictions[0])]
+        confidence = float(np.max(predictions[0])) * 100
+        
+        result_text = f"Prediction: {pred_class}\nConfidence: {confidence:.2f}%\n\n"
+        result_text += "All Probabilities:\n"
+        for i, name in enumerate(CLASS_NAMES):
+            result_text += f"  {name}: {predictions[0][i]*100:.2f}%\n"
+        
+        try:
+            heatmap = make_gradcam_heatmap(img_array, model)
+            heatmap = cv2.resize(heatmap, (224, 224))
+            heatmap = np.uint8(255 * heatmap)
+            heatmap_colored = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
+            original = np.array(img)
+            superimposed = cv2.addWeighted(original, 0.6, heatmap_colored, 0.4, 0)
+            output_image = superimposed
+        except Exception as e:
+            output_image = np.array(img)
+            result_text += f"\n(Grad-CAM unavailable: {str(e)})"
+        
+        return output_image, result_text
     
+    except Exception as e:
+        return None, f"Error: {str(e)}"
+
+def predict_from_image(input_image):
+    img = Image.fromarray(input_image).convert('RGB')
     img = img.resize((224, 224))
     img_array = np.array(img) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
@@ -75,16 +102,48 @@ def predict(input_image):
     
     return output_image, result_text
 
-demo = gr.Interface(
-    fn=predict,
+# Two interfaces: one for browser (image upload), one for API (base64)
+image_interface = gr.Interface(
+    fn=predict_from_image,
     inputs=gr.Image(label="Upload Histopathology Image"),
     outputs=[
         gr.Image(label="Grad-CAM Visualization"),
         gr.Textbox(label="Classification Result")
     ],
     title="Bone Cancer Detection (Osteosarcoma)",
-    description="Upload an H&E stained histopathology image to classify.",
-    api_name="predict"
+    description="Upload an H&E stained histopathology image."
+)
+
+api_interface = gr.Interface(
+    fn=predict_from_base64,
+    inputs=gr.Textbox(label="Base64 Image String"),
+    outputs=[
+        gr.Image(label="Grad-CAM Visualization"),
+        gr.Textbox(label="Classification Result")
+    ],
+    api_name="predict_base64"
+)
+
+demo = gr.TabbedInterface(
+    [image_interface, api_interface],
+    ["Upload Image", "API (Base64)"]
 )
 
-demo.launch()
+demo.launch()
+```
+
+---
+
+## What Changed:
+
+1. **Two interfaces** — one for browser upload, one for API with base64
+2. **New API endpoint** — `/predict_base64` accepts base64 text
+3. **Browser still works** — Users can still upload images normally
+
+---
+
+## After Update, Change n8n HTTP Request1:
+
+**URL:**
+```
+https://hixoop-model-v1.hf.space/gradio_api/call/predict_base64