import gradio as gr
import requests
import base64
import os
import time
import jwt
import logging
from pathlib import Path

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ===== API CONFIGURATION =====
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/generations"  # SINGLE image endpoint

# ===== AUTHENTICATION =====
def generate_jwt_token():
    payload = {
        "iss": ACCESS_KEY_ID,
        "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"""
    with open(image_path, "rb") as img_file:
        return base64.b64encode(img_file.read()).decode('utf-8')

# ===== API CALLS =====
def create_face_transform_task(image_base64, prompt):
    headers = {
        "Authorization": f"Bearer {generate_jwt_token()}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "model_name": "kling-v2",  # Best for face transformation
        "prompt": prompt,
        "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()
    except Exception as e:
        logger.error(f"API Error: {str(e)}")
        return None

def check_task_status(task_id):
    headers = {"Authorization": f"Bearer {generate_jwt_token()}"}
    try:
        response = requests.get(f"{API_BASE_URL}/v1/images/generations/{task_id}", headers=headers)
        response.raise_for_status()
        return response.json()
    except Exception as e:
        logger.error(f"Status Check Error: {str(e)}")
        return None

# ===== MAIN FUNCTION =====
def transform_face(image_path, prompt):
    if not image_path:
        return None, "Please upload an image first"
    
    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"
        
        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(img_data)
                return output_path, None
                
        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 Transformation")
    gr.Markdown("Upload a clear face photo and describe your transformation")
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="filepath", label="Upload Face Photo", sources=["upload"])
            prompt_input = gr.Textbox(
                label="Transformation Prompt", 
                placeholder="e.g. 'cyberpunk character', 'renaissance painting'"
            )
            generate_btn = gr.Button("Transform", variant="primary")
            
            gr.Markdown("### Requirements")
            gr.Markdown("""
            - Clear frontal face photo
            - Single person only
            - Max 10MB size (JPG/PNG)
            - Min 300x300 resolution
            """)
            
        with gr.Column():
            output_image = gr.Image(label="Transformed Result", interactive=False)
            output_file = gr.File(label="Download")
            status_output = gr.Textbox(label="Status", interactive=False)
    
    generate_btn.click(
        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)