Update app.py

app.py

@@ -1,69 +1,71 @@
 import gradio as gr
 import os
 import subprocess
-import requests
-import time
 
+# Define directories for uploads and outputs
 UPLOAD_FOLDER = 'uploads_gradio'
 OUTPUT_FOLDER = 'outputs_gradio'
 
+# Create directories if they don't exist
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)
 os.makedirs(OUTPUT_FOLDER, exist_ok=True)
 
-# Start TorchServe in the background
-# Adjust arguments to match your configuration
-ts_process = subprocess.Popen([
-    "torchserve", "--start",
-    "--model-store", "/home/torchserve/model-store",
-    "--ts-config", "/home/torchserve/config.properties",
-    "--no-config-snapshots"
-])
-
-# Wait for TorchServe to become healthy
-while True:
-    try:
-        r = requests.get("http://127.0.0.1:8080/ping")
-        if r.status_code == 200:
-            break
-    except:
-        pass
-    time.sleep(1)
-
 def animate_image(file_path):
-    # Here you can call TorchServe endpoints if needed, for example:
-    # response = requests.post("http://127.0.0.1:8080/predictions/drawn_humanoid_detector", files={"data": open(file_path, "rb")})
-    # Process the response and then run your animation logic.
-    # ...
-    # Or if you run your original script, just as before:
+    """
+    Function to process the uploaded image and generate an animated GIF.
     
+    Args:
+        file_path (str): Path to the uploaded file.
+    
+    Returns:
+        str: Path to the generated GIF.
+    """
     if not file_path:
         raise ValueError("No file uploaded.")
+
+    # 'file_path' is a string path to the uploaded file
     input_path = file_path
     filename = os.path.basename(input_path)
     base, ext = os.path.splitext(filename)
-    allowed_extensions = ['.png', '.jpg', '.jpeg', '.bmp']
-    if ext.lower() not in allowed_extensions:
-        raise ValueError("Unsupported file type.")
-
+    
+    # Define the annotation directory for this specific image
     char_anno_dir = os.path.join(OUTPUT_FOLDER, f"{base}_out")
     os.makedirs(char_anno_dir, exist_ok=True)
+    
+    try:
+        # Validate file extension
+        allowed_extensions = ['.png', '.jpg', '.jpeg', '.bmp']
+        if ext.lower() not in allowed_extensions:
+            raise ValueError("Unsupported file type. Please upload an image file (png, jpg, jpeg, bmp).")
+        
+        # Run the image_to_animation.py script with required arguments
+        subprocess.run([
+            'python', 'examples/image_to_animation.py',
+            input_path, char_anno_dir
+            # Optionally, add motion_cfg_fn and retarget_cfg_fn here if needed
+        ], check=True)
+        
+        # Path to the generated GIF
+        gif_path = os.path.join(char_anno_dir, "video.gif")
+        
+        if os.path.exists(gif_path):
+            return gif_path
+        else:
+            raise FileNotFoundError("Animation failed to generate. Please ensure the input image contains clear humanoid drawings.")
+    
+    except subprocess.CalledProcessError as e:
+        raise RuntimeError(f"Error during processing: {e}")
+    except Exception as e:
+        raise RuntimeError(f"Unexpected error: {e}")
 
-    subprocess.run(['python', 'examples/image_to_animation.py', input_path, char_anno_dir], check=True)
-
-    gif_path = os.path.join(char_anno_dir, "video.gif")
-    if os.path.exists(gif_path):
-        return gif_path
-    else:
-        raise FileNotFoundError("Animation failed to generate.")
-
+# Define the Gradio interface using the updated API
 iface = gr.Interface(
     fn=animate_image,
     inputs=gr.Image(label="Upload Drawing", type="filepath", sources=["upload", "webcam"]),
     outputs=gr.Image(label="Animated GIF"),
     title="Animated Drawings",
-    description="Upload or take a photo of a drawing, get an animated GIF."
+    description="Upload your drawing or take a photo, and get an animated GIF."
 )
 
 if __name__ == "__main__":
-    # Gradio on port 7860
-    iface.launch(server_name="0.0.0.0", server_port=7860)
+    iface.launch()