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| import gradio as gr | |
| import requests | |
| import base64 | |
| import os | |
| import time | |
| import jwt | |
| from pathlib import Path | |
| # Configuration - REPLACE WITH YOUR ACTUAL CREDENTIALS | |
| ACCESS_KEY_ID = "AFyHfnQATghFdCMyAG3gRPbNY4TNKFGB" | |
| ACCESS_KEY_SECRET = "TTepeLyBterLNM3brYPGmdndBnnyKJBA" | |
| API_BASE_URL = "https://api-singapore.klingai.com" | |
| ENDPOINT = f"{API_BASE_URL}/v1/images/generations" # Image-to-image endpoint | |
| def generate_jwt_token(): | |
| """Generate authentication token""" | |
| payload = { | |
| "iss": ACCESS_KEY_ID, | |
| "exp": int(time.time()) + 1800, # 30 min expiration | |
| "nbf": int(time.time()) - 5 # Not before 5 sec ago | |
| } | |
| return jwt.encode(payload, ACCESS_KEY_SECRET, algorithm="HS256") | |
| def process_image(image_path, prompt): | |
| """Core image processing function""" | |
| try: | |
| # 1. Validate image | |
| if not os.path.exists(image_path): | |
| return None, "Image file not found" | |
| if os.path.getsize(image_path) > 10 * 1024 * 1024: # 10MB | |
| return None, "Image too large (max 10MB)" | |
| # 2. Prepare image | |
| with open(image_path, "rb") as f: | |
| image_base64 = base64.b64encode(f.read()).decode('utf-8') | |
| # 3. API Request | |
| headers = { | |
| "Authorization": f"Bearer {generate_jwt_token()}", | |
| "Content-Type": "application/json" | |
| } | |
| payload = { | |
| "model_name": "kling-v2.1", | |
| "prompt": prompt, | |
| "image": image_base64, | |
| "image_reference": "face", | |
| "image_fidelity": 0.97, | |
| "human_fidelity": 0.97, | |
| "aspect_ratio": "1:1", | |
| "n": 1 | |
| } | |
| response = requests.post(ENDPOINT, json=payload, headers=headers) | |
| # 4. Handle response | |
| if response.status_code != 200: | |
| return None, f"API Error: {response.text}" | |
| data = response.json() | |
| if data.get("code") != 0: | |
| return None, f"API Error: {data.get('message', 'Unknown error')}" | |
| task_id = data["data"]["task_id"] | |
| # 5. Check task status (max 3 minutes) | |
| for _ in range(18): # 18 attempts × 10 seconds = 3 minutes | |
| time.sleep(10) | |
| status_response = requests.get( | |
| f"{API_BASE_URL}/v1/images/generations/{task_id}", | |
| headers=headers | |
| ) | |
| status_data = status_response.json() | |
| 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/result_{task_id}.png" | |
| with open(output_path, "wb") as f: | |
| f.write(img_data) | |
| return output_path, None | |
| elif status_data["data"]["task_status"] in ("failed", "canceled"): | |
| return None, status_data["data"].get("task_status_msg", "Task failed") | |
| return None, "Processing timed out" | |
| except Exception as e: | |
| return None, f"Error: {str(e)}" | |
| # Gradio Interface | |
| with gr.Blocks() as app: | |
| gr.Markdown("# 🖼️ Face Style Transformer") | |
| gr.Markdown("Upload a clear face photo and describe your desired style") | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image(type="filepath", label="Upload Face Photo") | |
| prompt_input = gr.Textbox(label="Style Prompt", | |
| placeholder="e.g. 'anime character', 'oil painting'") | |
| generate_btn = gr.Button("Transform", variant="primary") | |
| gr.Markdown("### Requirements:") | |
| gr.Markdown(""" | |
| - Clear frontal face photo | |
| - Single person only | |
| - Max 10MB (JPG/PNG) | |
| - Min 300x300 resolution | |
| """) | |
| with gr.Column(): | |
| output_image = gr.Image(label="Result", interactive=False) | |
| output_file = gr.File(label="Download Result") | |
| status_output = gr.Textbox(label="Status") | |
| generate_btn.click( | |
| fn=lambda img, prompt: process_image(img, prompt) + (None,), | |
| 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) |