Update app.py

app.py

@@ -7,39 +7,49 @@ import gradio as gr
 repo_url = "https://github.com/lvalics/roopapi.git"
 repo_dir = "roopapi"
 
-# Step 1: Clone the GitHub repository if it's not already present
+# Step 1: Clone the repository if it's not already present
 if not os.path.exists(repo_dir):
     print(f"Cloning the repository from {repo_url}...")
     subprocess.run(["git", "clone", repo_url])
 
-# Step 2: Install the necessary dependencies from the repository's requirements.txt
-# Ensure that we are in the correct directory before running pip install
+# Step 2: Navigate to the 'roopapi' directory and install dependencies
 os.chdir(repo_dir)
 
-# Installing the dependencies defined in the repository's requirements.txt
+# Install dependencies from 'requirements.txt' in the repository
 print("Installing dependencies from requirements.txt...")
 subprocess.run(["pip", "install", "-r", "requirements.txt"])
 
-# Step 3: Add the repository to Python's path to access the `roop` module
+# Step 3: Add the 'roopapi' directory to Python's module search path
 sys.path.append(os.path.join(os.getcwd(), repo_dir))
 
-# Step 4: Import the ROOP class from the `roop` module
+# Step 4: Import the ROOP module (from roopapi)
 try:
-    from roop import ROOP
+    from roop import ROOP  # Importing the ROOP class from the roopapi repo
     print("ROOP module imported successfully.")
 except ModuleNotFoundError as e:
-    print("Error importing the ROOP module:", e)
-    sys.exit(1)
+    print(f"Error importing the ROOP module: {e}")
+    sys.exit(1)  # Exit the script if ROOP is not found
 
-# Initialize the ROOP model (this might take time depending on model weights)
+# Step 5: Initialize the ROOP model
 roop_model = ROOP()
 
-# Step 5: Define a Gradio function to swap faces on an image and video
+# Step 6: Define a Gradio interface function for face swapping
 def process_face_swap(image, video):
-    # Use the ROOP model to perform face-swapping
+    # Use the ROOP model to swap faces
     print("Processing face swap...")
     output = roop_model.swap_faces(image, video)
     return output
 
-# Step 6: Create a Gradio interface
-iface = gr.Int
+# Step 7: Create a Gradio interface for the app
+iface = gr.Interface(
+    fn=process_face_swap,
+    inputs=["image", "video"],  # Image and video inputs
+    outputs="video",  # The output will be a video
+    live=True,
+    title="ROOP Face Swapping",
+    description="Upload an image and a video to swap faces using the ROOP model."
+)
+
+# Step 8: Launch the Gradio interface
+if __name__ == "__main__":
+    iface.launch()