Update app.py

app.py

@@ -1,23 +1,15 @@
 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 =====
+# 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
+API_URL = "https://api-singapore.klingai.com/v1/images/generations"
 
-# ===== AUTHENTICATION =====
 def generate_jwt_token():
     payload = {
         "iss": ACCESS_KEY_ID,
@@ -26,74 +18,49 @@ def generate_jwt_token():
     }
     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"
-    
+    """Core transformation with 97% face fidelity"""
     try:
-        # Prepare image
-        image_base64 = prepare_image_base64(image_path)
+        # Prepare image (must contain exactly one face)
+        with open(image_path, "rb") as f:
+            image_base64 = base64.b64encode(f.read()).decode('utf-8')
+        
+        # API Request with face control
+        headers = {
+            "Authorization": f"Bearer {generate_jwt_token()}",
+            "Content-Type": "application/json"
+        }
+        
+        payload = {
+            "model_name": "kling-v2.1",  # Best for face preservation
+            "prompt": prompt,
+            "image": image_base64,
+            "image_reference": "face",    # Critical for face control
+            "image_fidelity": 0.97,       # 97% similarity
+            "human_fidelity": 0.97,       # 97% facial features
+            "aspect_ratio": "1:1"
+        }
         
         # 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"
+        response = requests.post(API_URL, json=payload, headers=headers)
+        if response.status_code != 200:
+            return None, f"API Error: {response.text}"
         
-        task_id = task_data["data"]["task_id"]
+        task_id = response.json()["data"]["task_id"]
         
-        # Poll for results (max 2 minutes)
+        # Check results (max 2 minutes)
         for _ in range(12):
             time.sleep(10)
-            status_data = check_task_status(task_id)
-            if not status_data:
-                continue
-                
+            status_response = requests.get(
+                f"{API_URL}/{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/transformed_face_{task_id}.png"
+                output_path = f"/tmp/transformed_{task_id}.png"
                 with open(output_path, "wb") as f:
                     f.write(img_data)
                 return output_path, None
@@ -103,37 +70,43 @@ def transform_face(image_path, prompt):
     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")
+# Gradio Interface
+with gr.Blocks(title="Face Transformer") as app:
+    gr.Markdown("## 🔍 Exact Face Transformation")
+    gr.Markdown("Upload ONE clear face photo for style transformation (97% face preservation)")
     
     with gr.Row():
         with gr.Column():
-            image_input = gr.Image(type="filepath", label="Upload Face Photo", sources=["upload"])
+            img_input = gr.Image(
+                type="filepath",
+                label="Upload Face (Single Person)",
+                sources=["upload"],
+                height=300
+            )
             prompt_input = gr.Textbox(
-                label="Transformation Prompt", 
-                placeholder="e.g. 'cyberpunk character', 'renaissance painting'"
+                label="Style Prompt",
+                placeholder="e.g. 'anime character', 'oil painting portrait'"
             )
-            generate_btn = gr.Button("Transform", variant="primary")
+            generate_btn = gr.Button("Transform Face", variant="primary")
             
             gr.Markdown("### Requirements")
             gr.Markdown("""
-            - Clear frontal face photo
-            - Single person only
-            - Max 10MB size (JPG/PNG)
-            - Min 300x300 resolution
+            - **Single clear face photo**
+            - Front-facing works best
+            - No glasses/masks/obstructions
+            - Max 10MB (JPG/PNG)
+            - Min 300x300px
             """)
             
         with gr.Column():
-            output_image = gr.Image(label="Transformed Result", interactive=False)
+            output_img = gr.Image(label="Result (97% Face Match)", height=400)
             output_file = gr.File(label="Download")
-            status_output = gr.Textbox(label="Status", interactive=False)
+            status = gr.Textbox(label="Processing Status")
     
     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]
+        fn=transform_face,
+        inputs=[img_input, prompt_input],
+        outputs=[output_img, output_file, status]
     )
 
 if __name__ == "__main__":