update test script and ignore file

.gitignore

@@ -1 +1,2 @@
 venv
+__pycache__

huggingface_inference.py

@@ -0,0 +1,152 @@
+import requests
+import base64
+import os
+from PIL import Image
+import io
+import time
+import datetime
+
+def encode_image_to_base64(image_path):
+    """Convert an image file to base64 string."""
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+def process_image(
+    image_path, 
+    mask_path=None, 
+    prompt="", 
+    height=1632, 
+    width=1232, 
+    guidance_scale=30, 
+    num_inference_steps=50, 
+    max_sequence_length=512,
+    api_token=None,
+    output_path="output_image.jpg"
+):
+    """
+    Send a request to the Hugging Face Inference Endpoint.
+    
+    Args:
+        image_path (str): Path to the input image
+        mask_path (str, optional): Path to the mask image
+        prompt (str): Text prompt to guide the model
+        height (int): Output image height
+        width (int): Output image width
+        guidance_scale (float): Guidance scale for the model
+        num_inference_steps (int): Number of inference steps
+        max_sequence_length (int): Maximum sequence length
+        api_token (str): Hugging Face API token
+        output_path (str): Path to save the output image
+    
+    Returns:
+        The response from the API or the path to the saved image
+    """
+    # Log start time
+    start_time = time.time()
+    start_timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")
+    print(f"Request started at: {start_timestamp}")
+    
+    # Endpoint URL
+    url = "https://pewtn8bsankvfriv.us-east-1.aws.endpoints.huggingface.cloud"
+    
+    # Get API token (from environment variable or parameter)
+    if api_token is None:
+        api_token = os.environ.get("HF_API_TOKEN")
+        if api_token is None:
+            raise ValueError("API token not provided. Please set HF_API_TOKEN environment variable or pass it as a parameter.")
+    
+    # Encode image to base64
+    base64_image = encode_image_to_base64(image_path)
+    
+    # Encode mask to base64 if provided
+    base64_mask = None
+    if mask_path:
+        base64_mask = encode_image_to_base64(mask_path)
+    
+    # Prepare payload
+    payload = {
+        "inputs": {
+            "image": base64_image,
+            "prompt": prompt
+        },
+        "parameters": {
+            "height": height,
+            "width": width,
+            "guidance_scale": guidance_scale,
+            "num_inference_steps": num_inference_steps,
+            "max_sequence_length": max_sequence_length,
+        }
+    }
+    
+    # Add mask if provided
+    if base64_mask:
+        payload["inputs"]["mask"] = base64_mask
+    
+    # Set up headers
+    headers = {
+        "Authorization": f"Bearer {api_token}",
+        "Content-Type": "application/json"
+    }
+    
+    # Make the request
+    print(f"Sending request to {url}...")
+    response = requests.post(url, json=payload, headers=headers)
+    
+    # Log end time
+    end_time = time.time()
+    end_timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")
+    elapsed_time = end_time - start_time
+    print(f"Request completed at: {end_timestamp}")
+    print(f"Total request time: {elapsed_time:.2f} seconds ({elapsed_time/60:.2f} minutes)")
+    
+    # Handle the response
+    if response.status_code == 200:
+        try:
+            response_data = response.json()
+            
+            # Check if response has the expected format
+            if "image" in response_data and "status" in response_data and response_data["status"] == "success":
+                # Decode the base64 image
+                image_data = base64.b64decode(response_data["image"])
+                
+                # Convert to PIL Image
+                image = Image.open(io.BytesIO(image_data))
+                
+                # Save the image to the specified output path
+                image.save(output_path)
+                print(f"Image successfully saved to {output_path}")
+                
+                return output_path
+            else:
+                print("Unexpected response format:", response_data)
+                return response_data
+        except Exception as e:
+            print(f"Error processing response: {e}")
+            return response.json()
+    else:
+        print(f"Error: {response.status_code}")
+        print(response.text)
+        return None
+
+if __name__ == "__main__":
+    # Example usage
+    output_file = "generated_image1.jpg"
+    
+    # 从环境变量获取API令牌，或者提示用户输入
+    api_token = os.environ.get("HF_API_TOKEN")
+    if not api_token:
+        api_token = input("请输入您的Hugging Face API令牌: ")
+    
+    print("Starting image processing...")
+    result = process_image(
+        image_path="cup.png",
+        mask_path="cup_mask.png",  # Optional
+        prompt="a blue paper cup",
+        api_token=api_token,
+        output_path=output_file
+    )
+    
+    if result == output_file:
+        print(f"Processing completed successfully. Image saved to {output_file}")
+    else:
+        print("Processing completed with unexpected result:", result)