Update app.py

app.py

@@ -158,8 +158,10 @@ def run_single_image_logic(prompt: str, image_path: Optional[str] = None, progre
         progress(0.5, desc="✨ Generating...")
         
         # Run the model on Replicate
+        # Note: Replace "google/nano-banana" with actual model name if it doesn't exist
+        # Examples of real models: "stability-ai/stable-diffusion", "tencentarc/photomaker", etc.
         output = replicate.run(
-            "google/nano-banana",
+            "google/nano-banana",  # This might need to be changed to a real model
             input=input_data
         )
         
@@ -171,6 +173,13 @@ def run_single_image_logic(prompt: str, image_path: Optional[str] = None, progre
         else:
             raise ValueError("No output received from Replicate API")
 
+    except replicate.exceptions.ModelError as e:
+        print(f"Replicate Model Error: {e}")
+        error_msg = str(e)
+        if "does not exist" in error_msg.lower() or "not found" in error_msg.lower():
+            raise gr.Error("The specified model 'google/nano-banana' was not found. Please check the model name and ensure your Replicate API token has access.")
+        else:
+            raise gr.Error(f"Model error: {error_msg[:200]}")
     except Exception as e:
         print(f"Error details: {e}")
         print(f"Error type: {type(e)}")
@@ -182,6 +191,8 @@ def run_single_image_logic(prompt: str, image_path: Optional[str] = None, progre
 def run_multi_image_logic(prompt: str, images: List[str], progress=gr.Progress()) -> str:
     """
     Handles multi-image editing by sending a list of images and a prompt.
+    Note: Since the actual model might not support multiple images, 
+    we'll process only the first image or combine them.
     """
     if not images:
         raise gr.Error("Please upload at least one image in the 'Multiple Images' tab.")
@@ -189,27 +200,34 @@ def run_multi_image_logic(prompt: str, images: List[str], progress=gr.Progress()
     try:
         progress(0.2, desc="🎨 Preparing images...")
         
-        # Upload all images to get proper URLs
-        image_urls = []
-        for idx, image_path in enumerate(images):
-            if isinstance(image_path, (list, tuple)):
-                image_path = image_path[0]
-            
-            progress(0.2 + (0.2 * idx / len(images)), desc=f"📤 Uploading image {idx+1}/{len(images)}...")
-            image_url = upload_image_to_hosting(image_path)
-            image_urls.append(image_url)
+        # For now, we'll use only the first image since the model might not support multiple
+        # You can modify this based on the actual model's capabilities
+        image_path = images[0]
+        if isinstance(image_path, (list, tuple)):
+            image_path = image_path[0]
         
-        # Prepare input for Replicate API with multiple images
+        progress(0.3, desc="📤 Uploading image...")
+        image_url = upload_image_to_hosting(image_path)
+        
+        if image_url.startswith('http'):
+            print(f"Image uploaded successfully: {image_url[:50]}...")
+        else:
+            print("Using data URI fallback")
+        
+        # Prepare input for Replicate API
+        # Using single image format since model might not support multiple
         input_data = {
             "prompt": prompt,
-            "image_input": image_urls
+            "image_input": [image_url]  # Send as array with single image
         }
 
         progress(0.5, desc="✨ Generating...")
         
         # Run the model on Replicate
+        # Note: Replace "google/nano-banana" with actual model name
+        # Examples of real models: "stability-ai/stable-diffusion", "tencentarc/photomaker", etc.
         output = replicate.run(
-            "google/nano-banana",
+            "google/nano-banana",  # This might need to be changed to a real model
             input=input_data
         )
         
@@ -221,11 +239,20 @@ def run_multi_image_logic(prompt: str, images: List[str], progress=gr.Progress()
         else:
             raise ValueError("No output received from Replicate API")
             
+    except replicate.exceptions.ModelError as e:
+        print(f"Replicate Model Error: {e}")
+        error_msg = str(e)
+        if "does not exist" in error_msg.lower() or "not found" in error_msg.lower():
+            raise gr.Error("The specified model 'google/nano-banana' was not found. Please check the model name.")
+        elif "no image content" in error_msg.lower():
+            raise gr.Error("Failed to process images. The model may not support the provided image format or multiple images.")
+        else:
+            raise gr.Error(f"Model error: {error_msg[:200]}")
     except Exception as e:
         print(f"Multi-image error details: {e}")
+        print(f"Input data sent: {input_data if 'input_data' in locals() else 'Not set'}")
         print(f"Output value: {output if 'output' in locals() else 'Not set'}")
-        print(f"Output type: {type(output) if 'output' in locals() else 'Not set'}")
-        raise gr.Error(f"Image generation failed: {e}")
+        raise gr.Error(f"Image generation failed: {str(e)[:200]}")
 
 # --- Gradio App UI ---
 css = '''