Update requirements.txt

requirements.txt

@@ -1,82 +1,5 @@
-import os
-import cv2
-import numpy as np
-import onnxruntime as ort
-import gradio as gr
-from huggingface_hub import hf_hub_download
-
-# Ensure the Hugging Face token is retrieved from environment variables
-huggingface_token = os.getenv("HF_TOKEN")
-if huggingface_token is None:
-    raise ValueError("HUGGINGFACE_TOKEN environment variable not set.")
-
-# Download the model file from Hugging Face using the token
-model_id = "Arrcttacsrks/netrunner-exe_Insight-Swap-models-onnx"
-model_file = hf_hub_download(repo_id=model_id, filename="simswap_512_unoff.onnx", token=huggingface_token)
-
-def load_and_preprocess_image(image):
-    if image is None or not isinstance(image, np.ndarray):
-        raise ValueError("Input image is not valid.")
-    
-    img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    img = cv2.resize(img, (512, 512))
-    img = img / 255.0  # Normalize to [0, 1]
-    return img.astype(np.float32)
-
-def swap_faces(source_image, target_image):
-    try:
-        # Load the ONNX model
-        session = ort.InferenceSession(model_file)
-        
-        # Get input names
-        input_names = [input.name for input in session.get_inputs()]
-        
-        # Print input shapes for debugging
-        for input in session.get_inputs():
-            print(f"Input '{input.name}' expects shape: {input.shape}")
-        
-        # Preprocess the images
-        source_img = load_and_preprocess_image(source_image)
-        target_img = load_and_preprocess_image(target_image)
-        
-        # Reshape inputs according to model requirements
-        # For the first input (assuming it's the image input)
-        source_input = source_img.transpose(2, 0, 1)[np.newaxis, ...]  # Shape: (1, 3, 512, 512)
-        
-        # For the second input (onnx::Gemm_1), reshape to rank 2 as required by the error message
-        target_features = target_img.transpose(2, 0, 1).reshape(-1, 512)  # Reshape to 2D array
-        
-        # Create input dictionary
-        input_dict = {
-            input_names[0]: source_input.astype(np.float32),
-            input_names[1]: target_features.astype(np.float32)
-        }
-        
-        # Run inference
-        result = session.run(None, input_dict)[0]
-        
-        # Post-process the result
-        result = result[0].transpose(1, 2, 0)  # Convert from NCHW to HWC format
-        result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)  # Convert back to RGB
-        return np.clip(result * 255, 0, 255).astype(np.uint8)
-    
-    except Exception as e:
-        print(f"Error during face swapping: {str(e)}")
-        raise
-
-# Create Gradio interface
-interface = gr.Interface(
-    fn=swap_faces,
-    inputs=[
-        gr.Image(label="Source Face", type="numpy"),
-        gr.Image(label="Target Image", type="numpy")
-    ],
-    outputs=gr.Image(label="Result"),
-    title="Face Swap using SimSwap",
-    description="Upload a source face and a target image to swap faces. The source face will be transferred onto the target image.",
-    allow_flagging="never"
-)
-
-# Launch the interface
-if __name__ == "__main__":
-    interface.launch()
+onnxruntime 
+gradio 
+opencv-python 
+huggingface-hub 
+numpy