Update app.py

app.py

@@ -1,8 +1,8 @@
 import gradio as gr
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 from PIL import Image
-import requests
 import torch
+import numpy as np
 
 # Load the pre-trained model and processor
 processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
@@ -10,6 +10,7 @@ model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwrit
 
 # Define the prediction function
 def recognize_handwriting(image):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
     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]
@@ -23,13 +24,13 @@ def provide_feedback(image, correct_text):
 
 with gr.Blocks() as demo:
     with gr.Tab("Recognize Handwriting"):
-        image_input = gr.Image(type="pil")
+        sketchpad = gr.Sketchpad(label="Draw something")
         output = gr.Textbox(label="Recognized Text")
         recognize_button = gr.Button("Recognize")
-        recognize_button.click(fn=recognize_handwriting, inputs=image_input, outputs=output)
+        recognize_button.click(fn=recognize_handwriting, inputs=sketchpad, outputs=output)
 
     with gr.Tab("Provide Feedback"):
-        image_feedback = gr.Image(type="pil")
+        image_feedback = gr.Image(type="numpy")
         correct_text = gr.Textbox(label="Correct Text")
         feedback_button = gr.Button("Submit Feedback")
         feedback_output = gr.Textbox()