Update app.py

app.py

@@ -15,199 +15,126 @@ logger = logging.getLogger(__name__)
 ACCESS_KEY_ID = "AFyHfnQATghFdCMyAG3gRPbNY4TNKFGB"
 ACCESS_KEY_SECRET = "TTepeLyBterLNM3brYPGmdndBnnyKJBA"
 API_BASE_URL = "https://api-singapore.klingai.com"
-CREATE_TASK_ENDPOINT = f"{API_BASE_URL}/v1/images/multi-image2image"
+CREATE_TASK_ENDPOINT = f"{API_BASE_URL}/v1/images/generations"  # SINGLE image endpoint
 
 # ===== AUTHENTICATION =====
 def generate_jwt_token():
-    """Generate JWT token for API authentication"""
     payload = {
         "iss": ACCESS_KEY_ID,
-        "exp": int(time.time()) + 1800,  # 30 minutes expiration
-        "nbf": int(time.time()) - 5      # Not before 5 seconds ago
+        "exp": int(time.time()) + 1800,
+        "nbf": int(time.time()) - 5
     }
     return jwt.encode(payload, ACCESS_KEY_SECRET, algorithm="HS256")
 
 # ===== IMAGE PROCESSING =====
 def prepare_image_base64(image_path):
     """Convert image to base64 without prefix"""
-    try:
-        with open(image_path, "rb") as img_file:
-            return base64.b64encode(img_file.read()).decode('utf-8')
-    except Exception as e:
-        logger.error(f"Image processing failed: {str(e)}")
-        return None
-
-def validate_image(image_path):
-    """Validate image meets API requirements"""
-    try:
-        # Check file size
-        size_mb = os.path.getsize(image_path) / (1024 * 1024)
-        if size_mb > 10:
-            return False, "Image too large (max 10MB)"
-        
-        # Check dimensions (basic check - should use PIL for actual dimensions)
-        return True, ""
-    except Exception as e:
-        return False, f"Image validation error: {str(e)}"
+    with open(image_path, "rb") as img_file:
+        return base64.b64encode(img_file.read()).decode('utf-8')
 
-# ===== API FUNCTIONS =====
-def create_multi_image_task(subject_images, prompt):
-    """Create multi-image generation task"""
+# ===== API CALLS =====
+def create_face_transform_task(image_base64, prompt):
     headers = {
         "Authorization": f"Bearer {generate_jwt_token()}",
         "Content-Type": "application/json"
     }
     
-    # Prepare subject images list
-    subject_image_list = []
-    for img_path in subject_images:
-        if img_path:  # Skip empty/None images
-            base64_img = prepare_image_base64(img_path)
-            if base64_img:
-                subject_image_list.append({"subject_image": base64_img})
-    
-    if len(subject_image_list) < 2:
-        return None, "At least 2 subject images required"
-    
     payload = {
-        "model_name": "kling-v2",
+        "model_name": "kling-v2",  # Best for face transformation
         "prompt": prompt,
-        "subject_image_list": subject_image_list,
-        "n": 1,
+        "image": image_base64,
+        "image_reference": "face",  # Critical for face transformation
+        "image_fidelity": 0.97,     # 97% face similarity
+        "human_fidelity": 0.95,     # High facial feature preservation
         "aspect_ratio": "1:1"
     }
     
     try:
         response = requests.post(CREATE_TASK_ENDPOINT, json=payload, headers=headers)
         response.raise_for_status()
-        return response.json(), None
-    except requests.exceptions.RequestException as e:
-        logger.error(f"API request failed: {str(e)}")
-        if hasattr(e, 'response') and e.response:
-            logger.error(f"API response: {e.response.text}")
-        return None, f"API Error: {str(e)}"
+        return response.json()
+    except Exception as e:
+        logger.error(f"API Error: {str(e)}")
+        return None
 
 def check_task_status(task_id):
-    """Check task completion status"""
     headers = {"Authorization": f"Bearer {generate_jwt_token()}"}
-    status_url = f"{API_BASE_URL}/v1/images/multi-image2image/{task_id}"
-    
     try:
-        response = requests.get(status_url, headers=headers)
+        response = requests.get(f"{API_BASE_URL}/v1/images/generations/{task_id}", headers=headers)
         response.raise_for_status()
-        return response.json(), None
-    except requests.exceptions.RequestException as e:
-        return None, f"Status check failed: {str(e)}"
+        return response.json()
+    except Exception as e:
+        logger.error(f"Status Check Error: {str(e)}")
+        return None
 
-# ===== MAIN PROCESSING =====
-def generate_image(subject_images, prompt):
-    """Handle complete image generation workflow"""
-    # Validate images
-    for img in subject_images:
-        if img:  # Only validate non-empty images
-            is_valid, error_msg = validate_image(img)
-            if not is_valid:
-                return None, error_msg
-    
-    # Create task
-    task_response, error = create_multi_image_task(subject_images, prompt)
-    if error:
-        return None, error
-    
-    if task_response.get("code") != 0:
-        return None, f"API error: {task_response.get('message', 'Unknown error')}"
+# ===== MAIN FUNCTION =====
+def transform_face(image_path, prompt):
+    if not image_path:
+        return None, "Please upload an image first"
     
-    task_id = task_response["data"]["task_id"]
-    logger.info(f"Task created: {task_id}")
-    
-    # Poll for results (max 10 minutes)
-    for _ in range(60):
-        task_data, error = check_task_status(task_id)
-        if error:
-            return None, error
-            
-        status = task_data["data"]["task_status"]
+    try:
+        # Prepare image
+        image_base64 = prepare_image_base64(image_path)
+        
+        # Create task
+        task_data = create_face_transform_task(image_base64, prompt)
+        if not task_data or task_data.get("code") != 0:
+            return None, "Failed to start transformation"
         
-        if status == "succeed":
-            image_url = task_data["data"]["task_result"]["images"][0]["url"]
-            try:
-                response = requests.get(image_url)
-                response.raise_for_status()
-                output_path = Path(f"/tmp/kling_output_{task_id}.png")
+        task_id = task_data["data"]["task_id"]
+        
+        # Poll for results (max 2 minutes)
+        for _ in range(12):
+            time.sleep(10)
+            status_data = check_task_status(task_id)
+            if not status_data:
+                continue
+                
+            if status_data["data"]["task_status"] == "succeed":
+                image_url = status_data["data"]["task_result"]["images"][0]["url"]
+                img_data = requests.get(image_url).content
+                output_path = f"/tmp/transformed_face_{task_id}.png"
                 with open(output_path, "wb") as f:
-                    f.write(response.content)
-                return str(output_path), None
-            except Exception as e:
-                return None, f"Failed to download result: {str(e)}"
+                    f.write(img_data)
+                return output_path, None
                 
-        elif status in ("failed", "canceled"):
-            error_msg = task_data["data"].get("task_status_msg", "Unknown error")
-            return None, f"Task failed: {error_msg}"
-            
-        time.sleep(10)
-    
-    return None, "Task timed out after 10 minutes"
+        return None, "Processing timed out"
+        
+    except Exception as e:
+        return None, f"Error: {str(e)}"
 
 # ===== GRADIO INTERFACE =====
-def process_interface(subject_image1, subject_image2, subject_image3, subject_image4, prompt):
-    # Filter out None/empty images
-    subject_images = [img for img in [subject_image1, subject_image2, subject_image3, subject_image4] if img]
-    
-    if len(subject_images) < 2:
-        return None, None, "Please upload at least 2 subject images"
-    
-    output_path, error = generate_image(subject_images, prompt)
-    if error:
-        return None, None, error
-    
-    return output_path, output_path, "Generation successful!"
-
-with gr.Blocks(title="Kling AI Multi-Image Generator") as app:
-    gr.Markdown("## 🖼️ Kling AI Multi-Image to Image")
-    gr.Markdown("Combine features from multiple images into one result")
+with gr.Blocks() as app:
+    gr.Markdown("# 🎭 Face Transformation")
+    gr.Markdown("Upload a clear face photo and describe your transformation")
     
     with gr.Row():
         with gr.Column():
-            gr.Markdown("### Input Settings")
-            with gr.Row():
-                subject_image1 = gr.Image(type="filepath", label="Subject Image 1 *")
-                subject_image2 = gr.Image(type="filepath", label="Subject Image 2 *")
-            with gr.Row():
-                subject_image3 = gr.Image(type="filepath", label="Subject Image 3 (Optional)")
-                subject_image4 = gr.Image(type="filepath", label="Subject Image 4 (Optional)")
-            
+            image_input = gr.Image(type="filepath", label="Upload Face Photo", sources=["upload"])
             prompt_input = gr.Textbox(
-                label="Transformation Prompt",
-                placeholder="Describe how to combine these images"
+                label="Transformation Prompt", 
+                placeholder="e.g. 'cyberpunk character', 'renaissance painting'"
             )
+            generate_btn = gr.Button("Transform", variant="primary")
             
-            generate_btn = gr.Button("Generate", variant="primary")
-            
-            gr.Markdown("### Requirements (* = required)")
+            gr.Markdown("### Requirements")
             gr.Markdown("""
-            - **At least 2 subject images** (marked with *)
-            - Max 4 images total
-            - Max size: 10MB per image
-            - Formats: JPG, PNG
-            - Min dimensions: 300x300px
+            - Clear frontal face photo
+            - Single person only
+            - Max 10MB size (JPG/PNG)
+            - Min 300x300 resolution
             """)
             
         with gr.Column():
-            gr.Markdown("### Output")
-            output_image = gr.Image(label="Generated Image", interactive=False, height=400)
-            output_file = gr.File(label="Download Result")
+            output_image = gr.Image(label="Transformed Result", interactive=False)
+            output_file = gr.File(label="Download")
             status_output = gr.Textbox(label="Status", interactive=False)
     
-    # Modified inputs to accept individual components
     generate_btn.click(
-        fn=process_interface,
-        inputs=[subject_image1, subject_image2, subject_image3, subject_image4, prompt_input],
+        fn=lambda img, prompt: transform_face(img, prompt) + (None,),  # Extra None for error placeholder
+        inputs=[image_input, prompt_input],
         outputs=[output_image, output_file, status_output]
     )
 
 if __name__ == "__main__":
-    app.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False
-    )
+    app.launch(server_name="0.0.0.0", server_port=7860)