lmao lets goooo

app.py

@@ -1,18 +1,53 @@
 import gradio as gr
+import torch
+import numpy as np
+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
 
 def process_image(input_image, learning_rate, iterations):
-    # Your image processing logic here
-    output_image = input_image  # Placeholder for your image processing
-    return output_image
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    feature_extractor = ViTImageProcessor.from_pretrained('google/vit-large-patch32-384')
+    model = ViTForImageClassification.from_pretrained('google/vit-large-patch32-384')
+    model.to(device)
+    model.eval()
+
+    def get_encoder_activations(x):
+        encoder_output = model.vit(x)
+        final_activations = encoder_output.last_hidden_state
+        return final_activations
+    
+    image = input_image.convert('RGB')
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+    pixel_values.to(device)
+    pixel_values.requires_grad_(True)
+
+    for iteration in range(iterations.value):
+        model.zero_grad()
+        if pixel_values.grad is not None:
+            pixel_values.grad.data.zero_()
+
+        final_activations = get_encoder_activations(pixel_values.to('cuda'))
+        target_sum = final_activations.sum()
+        target_sum.backward()
+
+        with torch.no_grad():
+            pixel_values.data += learning_rate.value * pixel_values.grad.data
+        pixel_values.data = torch.clamp(pixel_values.data, -1, 1)
+
+    updated_pixel_values_np = 127.5 + pixel_values.squeeze().permute(1, 2, 0).detach().cpu() * 127.5
+    updated_pixel_values_np = updated_pixel_values_np.numpy().astype(np.uint8)
+
+    return updated_pixel_values_np
 
 iface = gr.Interface(
     fn=process_image,
     inputs=[
-        gr.Image(), 
+        gr.Image(type="pil"), 
         gr.Number(value=0.01, label="Learning Rate"), 
         gr.Number(value=1, label="Iterations")
     ],
-    outputs=gr.Image()
+    outputs=gr.Image(type="numpy", label="Processed Image")
 )
 
 iface.launch()

requirements.txt

@@ -1 +1,5 @@
-gradio
+gradio
+torch
+numpy
+transformers
+PIL