Upload 2 files

main.py

@@ -98,35 +98,23 @@ class ImagePredictor:
             raise RuntimeError("Model loading failed.") from e
 
     # --- Prediction Method ---
-    def predict(self, image: Image.Image):
-        """
-        Takes a PIL image, preprocesses it, and returns label probabilities.
-        Uses the loaded instance attributes (self.model, self.transforms, etc.)
-        """
-        # Check if initialization succeeded (should be caught by __init__ exceptions, but good practice)
-        if self.model is None or self.transforms is None or self.id2label is None:
-            return {"Error": "Predictor not initialized correctly. Check logs."}
-        if image is None:
-            return None # Gradio handles None input
-
-        try:
-            # Preprocess the image
-            image = image.convert("RGB") # Ensure 3 channels
-            pixel_values = self.transforms(image).unsqueeze(0).to(self.device)
-
-            # Perform inference
-            with torch.no_grad():
-                outputs = self.model(pixel_values=pixel_values)
-                logits = outputs.logits
-
-            # Get probabilities and format output
-            probabilities = F.softmax(logits, dim=-1)[0] # Get probabilities for the first image
-            confidences = {self.id2label[i]: float(prob) for i, prob in enumerate(probabilities)}
-            return confidences
-
-        except Exception as e:
-            print(f"Error during prediction: {e}")
-            return {"Error": f"Prediction failed: {str(e)}"}
+# Inside the ImagePredictor class:
+def predict(self, image: Image.Image):
+    print("--- Predict function called ---") # Check if this even prints in Space logs
+    if image is None:
+        print("Input image is None")
+        return None
+    try:
+        # Simulate some processing time
+        import time
+        time.sleep(0.1)
+        # Return a dummy dictionary, bypassing all model/transform logic
+        dummy_output = {"fake": 0.6, "real": 0.4} # Use your actual labels
+        print(f"Returning dummy output: {dummy_output}")
+        return dummy_output
+    except Exception as e:
+        print(f"Error in *simplified* predict: {e}")
+        return {"Error": f"Simplified prediction failed: {str(e)}"}
 
 
 # --- Main Execution Logic ---
@@ -159,7 +147,7 @@ if predictor and predictor.model: # Check if predictor initialized successfully
 
         print("Launching Gradio interface...")
         # Set share=True as requested
-        iface.launch(share=True, debug=True, show_error=True)
+        iface.launch(share=True, debug=True, show_error=True).queue()
 
     except Exception as e:
         print(f"Error creating or launching Gradio interface: {e}")

requirements.txt

@@ -1,5 +1,7 @@
-gradio 
-torchvision
-transformers
-tensorflow
-torch
+transformers>=4.30.0 # Or a specific recent version
+torch>=2.0.0        # Or a specific recent version compatible with transformers & cuda version in space
+torchvision>=0.15.0 # Or a specific recent version compatible with torch
+gradio>=4.0.0        # Or a specific recent version
+Pillow>=9.0.0
+accelerate>=0.20.0   # Often helpful, especially with safetensors
+numpy