Delete app.py

app.py

@@ -1,35 +0,0 @@
-# app.py
-import torch
-import torch.nn.functional as F
-import gradio as gr
-import numpy as np
-from PIL import Image
-from model import CNN
-
-# Load model
-model = CNN()
-model.load_state_dict(torch.load("pytorch_model.bin", map_location="cpu"))
-model.eval()
-
-# Inference function
-def predict_digit(image):
-    image = image.convert("L").resize((28, 28))        # Convert to grayscale
-    image = np.array(image) / 255.0                     # Normalize
-    image = torch.tensor(image).unsqueeze(0).unsqueeze(0).float()  # (1, 1, 28, 28)
-    with torch.no_grad():
-        logits = model(image)
-        probs = F.softmax(logits, dim=1).numpy().flatten()
-        predicted = np.argmax(probs)
-    return {str(i): float(probs[i]) for i in range(10)}
-
-# Gradio UI
-interface = gr.Interface(
-    fn=predict_digit,
-    inputs=gr.Image(type="pil", shape=(280, 280), tool="editor"),
-    outputs=gr.Label(num_top_classes=3),
-    title="Handwritten Digit Classifier",
-    description="Draw a digit or upload a digit image."
-)
-
-if __name__ == "__main__":
-    interface.launch()