Update app.py

app.py

@@ -60,123 +60,83 @@ def upload_image_to_hosting(image):
 
 def process_images(prompt, image1, image2=None):
     """
-    Process uploaded images with Replicate API or generate from text
+    Process with Nano Banana - works with or without images
     """
     if not os.getenv('REPLICATE_API_TOKEN'):
         return None, "Please set REPLICATE_API_TOKEN"
     
     try:
-        # Check if any images are uploaded
-        if not image1 and not image2:
-            # Text-to-Image mode (no images uploaded)
-            return generate_from_text(prompt)
+        # Prepare input for Nano Banana model
+        model_input = {
+            "prompt": prompt
+        }
         
-        # Image-to-Image mode (at least one image uploaded)
-        image_urls = []
-        
-        # Upload images
-        if image1:
-            url1 = upload_image_to_hosting(image1)
-            image_urls.append(url1)
-        
-        if image2:
-            url2 = upload_image_to_hosting(image2)
-            image_urls.append(url2)
+        # Only add image_input if images are provided
+        if image1 or image2:
+            image_urls = []
+            
+            if image1:
+                url1 = upload_image_to_hosting(image1)
+                image_urls.append(url1)
+            
+            if image2:
+                url2 = upload_image_to_hosting(image2)
+                image_urls.append(url2)
+            
+            model_input["image_input"] = image_urls
+            status_msg = "✨ Generated with style transfer!"
+        else:
+            # No images - text-only generation with Nano Banana
+            status_msg = "✨ Generated from text!"
         
-        # Run the model with images
+        # Run Nano Banana model (it should handle both cases)
         output = replicate.run(
             "google/nano-banana",
-            input={
-                "prompt": prompt,
-                "image_input": image_urls
-            }
+            input=model_input
         )
         
-        return process_output(output)
+        if output is None:
+            return None, "No output received"
         
-    except Exception as e:
-        return None, f"Error: {str(e)[:100]}"
-
-def generate_from_text(prompt):
-    """
-    Generate image from text only using a text-to-image model
-    """
-    try:
-        # Use a text-to-image model when no images are provided
-        # Using SDXL-Lightning for fast generation
-        output = replicate.run(
-            "bytedance/sdxl-lightning-4step:5599ed30703defd1d160a25a63321b4dec97101d98b4674bcc56e41f62f35637",
-            input={
-                "prompt": prompt,
-                "negative_prompt": "worst quality, low quality, blurry, nsfw",
-                "width": 1024,
-                "height": 1024,
-                "num_inference_steps": 4,
-                "guidance_scale": 0,
-                "scheduler": "K_EULER"
-            }
-        )
-        
-        return process_output(output, "✨ Generated from text successfully!")
+        # Get the generated image
+        try:
+            if hasattr(output, 'read'):
+                img_data = output.read()
+                img = Image.open(BytesIO(img_data))
+                return img, status_msg
+        except:
+            pass
         
-    except Exception as e:
-        # Fallback to another model if the first one fails
         try:
-            output = replicate.run(
-                "stability-ai/sdxl:39ed52f2a78e934b3ba6e2a89f5b1c712de7dfea535525255b1aa35c5565e08b",
-                input={
-                    "prompt": prompt,
-                    "negative_prompt": "worst quality, low quality",
-                    "width": 1024,
-                    "height": 1024,
-                    "num_inference_steps": 25,
-                    "refine": "expert_ensemble_refiner",
-                    "apply_watermark": False
-                }
-            )
-            return process_output(output, "✨ Generated from text successfully!")
-        except Exception as e2:
-            return None, f"Text generation error: {str(e2)[:100]}"
-
-def process_output(output, success_message="✨ Generated successfully!"):
-    """
-    Process the output from Replicate API
-    """
-    if output is None:
-        return None, "No output received"
-    
-    # Get the generated image
-    try:
-        if hasattr(output, 'read'):
-            img_data = output.read()
-            img = Image.open(BytesIO(img_data))
-            return img, success_message
-    except:
-        pass
-    
-    try:
-        if hasattr(output, 'url'):
-            output_url = output.url()
+            if hasattr(output, 'url'):
+                output_url = output.url()
+                response = requests.get(output_url, timeout=30)
+                if response.status_code == 200:
+                    img = Image.open(BytesIO(response.content))
+                    return img, status_msg
+        except:
+            pass
+        
+        output_url = None
+        if isinstance(output, str):
+            output_url = output
+        elif isinstance(output, list) and len(output) > 0:
+            output_url = output[0]
+        
+        if output_url:
             response = requests.get(output_url, timeout=30)
             if response.status_code == 200:
                 img = Image.open(BytesIO(response.content))
-                return img, success_message
-    except:
-        pass
-    
-    output_url = None
-    if isinstance(output, str):
-        output_url = output
-    elif isinstance(output, list) and len(output) > 0:
-        output_url = output[0]
-    
-    if output_url:
-        response = requests.get(output_url, timeout=30)
-        if response.status_code == 200:
-            img = Image.open(BytesIO(response.content))
-            return img, success_message
-    
-    return None, "Could not process output"
+                return img, status_msg
+        
+        return None, "Could not process output"
+        
+    except Exception as e:
+        error_msg = str(e)
+        if "image_input" in error_msg.lower():
+            # If the model requires images, provide a helpful message
+            return None, "Note: This model may require at least one image. Try uploading an image or check the error: " + error_msg[:100]
+        return None, f"Error: {error_msg[:100]}"
 
 # Enhanced CSS with modern, minimal design
 css = """
@@ -321,7 +281,7 @@ with gr.Blocks(css=css, theme=gr.themes.Base()) as demo:
             <h1 class="logo-text">🍌 Free Nano Banana</h1>
             <p class="subtitle">AI-Powered Image Generation & Style Transfer</p>
             <div class="mode-indicator">
-                💡 Works in two modes: Text-to-Image (no upload) or Style Transfer (with images)
+                💡 Works with or without images - Just describe what you want!
             </div>
             <div style="display: flex; justify-content: center; align-items: center; gap: 10px; margin-top: 20px;">
                 <a href="https://huggingface.co/spaces/ginigen/Nano-Banana-PRO" target="_blank">
@@ -346,9 +306,10 @@ with gr.Blocks(css=css, theme=gr.themes.Base()) as demo:
         # Info box
         gr.HTML("""
             <div class="info-box">
-                <strong>How to use:</strong><br>
-                • <b>Text-to-Image:</b> Enter a prompt without uploading images<br>
-                • <b>Style Transfer:</b> Upload 1-2 images and describe the style you want
+                <strong>How to use Nano Banana:</strong><br>
+                • <b>Text Generation:</b> Just enter a prompt - no images needed!<br>
+                • <b>Style Transfer:</b> Add images to apply specific styles<br>
+                • The powerful Nano Banana model handles both modes seamlessly
             </div>
         """)
         
@@ -357,9 +318,9 @@ with gr.Blocks(css=css, theme=gr.themes.Base()) as demo:
             with gr.Column(scale=1):
                 prompt = gr.Textbox(
                     label="Prompt / Style Description",
-                    placeholder="Describe what you want to generate or the style to apply...",
+                    placeholder="Describe what you want to generate...",
                     lines=3,
-                    value="A beautiful sunset over mountains with golden clouds",
+                    value="A beautiful banana-themed paradise with golden sunset",
                     elem_classes="prompt-input"
                 )
                 
@@ -397,7 +358,7 @@ with gr.Blocks(css=css, theme=gr.themes.Base()) as demo:
                     interactive=False,
                     lines=1,
                     elem_classes="status-text",
-                    value="Ready to generate..."
+                    value="Ready to generate with Nano Banana..."
                 )
     
     # Event handler
@@ -407,16 +368,17 @@ with gr.Blocks(css=css, theme=gr.themes.Base()) as demo:
         outputs=[output_image, status]
     )
     
-    # Examples
+    # Examples for text-only generation
     gr.Examples(
         examples=[
-            ["A cyberpunk city at night with neon lights and flying cars", None, None],
-            ["A magical forest with glowing mushrooms and fireflies", None, None],
-            ["Portrait of a robot in renaissance painting style", None, None],
-            ["Underwater palace made of coral and pearls", None, None],
+            ["A majestic banana kingdom floating in the clouds", None, None],
+            ["Cyberpunk banana city with neon lights", None, None],
+            ["Ancient banana temple in a mystical forest", None, None],
+            ["Banana spaceship exploring the cosmos", None, None],
+            ["Make the sheets in the style of the logo. Make the scene natural.", None, None],
         ],
         inputs=[prompt, image1, image2],
-        label="Text-to-Image Examples"
+        label="Text Generation Examples (No Images Needed!)"
     )
 
 # Launch