Create app.py

app.py

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+import gradio as gr
+import requests
+import base64
+from PIL import Image
+import io
+import time
+import os
+
+# Your endpoint URL and token (you might want to use environment variables for these in production)
+API_URL = "https://ca80xvp8jmhqanbz.us-east-1.aws.endpoints.huggingface.cloud"
+
+def process_image(input_image, prompt):
+    # Convert Gradio's image input to base64
+    if input_image is None:
+        return None, "Please upload an image"
+    
+    # Convert PIL Image to bytes
+    img_byte_arr = io.BytesIO()
+    input_image.save(img_byte_arr, format='WEBP')
+    img_byte_arr = img_byte_arr.getvalue()
+    
+    # Encode to base64
+    base64_image = f"data:image/webp;base64,{base64.b64encode(img_byte_arr).decode('utf-8')}"
+    
+    # Prepare headers and data
+    headers = {
+        "Authorization": f"Bearer {os.getenv('HF_API_KEY')}",
+        "Content-Type": "application/json",
+        "Accept": "application/json"
+    }
+    
+    data = {
+        "inputs": {
+            "prompt": prompt,
+            "image": base64_image
+        }
+    }
+    
+    # Make the API request with retry mechanism
+    max_retries = 5
+    retry_delay = 60  # seconds
+    
+    for retry in range(max_retries):
+        try:
+            response = requests.post(API_URL, headers=headers, json=data, timeout=300)
+            
+            if response.status_code == 200:
+                # Convert the response base64 image back to PIL Image
+                result = response.json()
+                image_bytes = base64.b64decode(result["final_image"])
+                output_image = Image.open(io.BytesIO(image_bytes))
+                
+                # Return both the image and the parameters used
+                params_text = "\nParameters used:\n"
+                for key, value in result["parameters"].items():
+                    params_text += f"{key}: {value}\n"
+                
+                return output_image, params_text
+                
+            elif response.status_code == 503:
+                if retry < max_retries - 1:
+                    time.sleep(retry_delay)
+                    continue
+                return None, f"Service unavailable after {max_retries} retries"
+            else:
+                return None, f"Error: {response.status_code}\n{response.text}"
+                
+        except requests.exceptions.RequestException as e:
+            if retry < max_retries - 1:
+                time.sleep(retry_delay)
+                continue
+            return None, f"Request error: {str(e)}"
+    
+    return None, "Maximum retries reached"
+
+# Create Gradio interface
+with gr.Blocks(title="Room Design Diffusion", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # Room Design Diffusion
+    Upload a room image and provide a prompt describing how you'd like to redesign it.
+    The AI will generate a new version of your room based on your description.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Upload Room Image", type="pil")
+            prompt = gr.Textbox(
+                label="Describe how you want to redesign the room",
+                placeholder="Example: A modern living room with a comfortable gray sectional sofa, glass coffee table, minimalist TV stand, and geometric area rug",
+                lines=3
+            )
+            submit_btn = gr.Button("Generate Design", variant="primary")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Generated Design")
+            output_text = gr.Textbox(label="Processing Details", lines=5)
+    
+    submit_btn.click(
+        fn=process_image,
+        inputs=[input_image, prompt],
+        outputs=[output_image, output_text]
+    )
+    
+    gr.Markdown("""
+    ### Tips for best results:
+    - Use clear, detailed prompts describing the desired room style
+    - Make sure the input image is well-lit and clearly shows the room
+    - Be patient as generation can take a few minutes
+    """)
+
+# Launch the app
+demo.launch(share=True)