Delete app.py

app.py

@@ -1,41 +0,0 @@
-import gradio as gr
-from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-from PIL import Image
-import torch
-
-# Path to the model folder bundled inside this Space.
-# Upload the full contents of your saved model directory
-# (the one you got from model.save_pretrained(...) / processor.save_pretrained(...)
-# in Week 4) into a folder here called "model" so this path resolves.
-MODEL_PATH = "./model"
-
-processor = TrOCRProcessor.from_pretrained(MODEL_PATH)
-model = VisionEncoderDecoderModel.from_pretrained(MODEL_PATH)
-model.eval()
-
-
-def extract_urdu_text(image):
-    """Takes an image, returns extracted Urdu text."""
-    if image is None:
-        return "Please upload an image"
-
-    pixel_values = processor(image, return_tensors="pt").pixel_values
-    with torch.no_grad():
-        generated_ids = model.generate(pixel_values, max_new_tokens=128)
-    text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-    return text if text.strip() else "Could not extract text from this image"
-
-
-interface = gr.Interface(
-    fn=extract_urdu_text,
-    inputs=gr.Image(type="pil", label="Upload Urdu Image"),
-    outputs=gr.Textbox(label="Extracted Urdu Text"),
-    title="Urdu OCR -- Code Saviours SI-26",
-    description="Upload an image containing Urdu text and get the extracted text.",
-    examples=[],
-)
-
-if __name__ == "__main__":
-    # On Spaces you don't need share=True -- Spaces already gives you a public URL.
-    interface.launch()