Create app.py

app.py

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+import gradio as gr
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import requests
+#import torch
+
+# Load the processor and model
+processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-printed')
+model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-printed')
+
+def generate_text(input_image):
+    # Convert Gradio input image to PIL Image
+    image = Image.fromarray(input_image)
+    # Process the image and generate text
+    pixel_values = processor(images=image, return_tensors="pt").pixel_values
+    generated_ids = model.generate(pixel_values)
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return generated_text
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Image(),  # Gradio Image input
+    outputs=gr.Textbox(),  # Gradio Textbox output
+)
+
+if __name__ == "__main__":
+    iface.launch()