Update app.py

app.py

@@ -1,71 +1,69 @@
 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):
-    """
-    Function to process the uploaded image and generate an animated GIF.
+    # 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:
     
-    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)
-    
-    # Define the annotation directory for this specific image
+    allowed_extensions = ['.png', '.jpg', '.jpeg', '.bmp']
+    if ext.lower() not in allowed_extensions:
+        raise ValueError("Unsupported file type.")
+
     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}")
 
-# Define the Gradio interface using the updated API
+    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.")
+
 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 your drawing or take a photo, and get an animated GIF."
+    description="Upload or take a photo of a drawing, get an animated GIF."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    # Gradio on port 7860
+    iface.launch(server_name="0.0.0.0", server_port=7860)