Update app.py

app.py

@@ -1,62 +1,38 @@
 import gradio as gr
-import numpy as np
-import os
-import pickle
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 from PIL import Image
-from transformers import pipeline
-
-# Load the classifier model
-classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
-
-# Initialize a dictionary to store feedback
-feedback_data = {"images": [], "labels": []}
-
-def save_feedback():
-    with open("feedback_data.pkl", "wb") as f:
-        pickle.dump(feedback_data, f)
-
-def load_feedback():
-    global feedback_data
-    if os.path.exists("feedback_data.pkl"):
-        with open("feedback_data.pkl", "rb") as f:
-            feedback_data = pickle.load(f)
-
-def predict(image):
-    image = Image.fromarray(image.astype('uint8'), 'RGB')
-    prediction = classifier(image)
-    return {pred["label"]: pred["score"] for pred in prediction}
-
-def provide_feedback(image, label):
-    global feedback_data
-    feedback_data["images"].append(image)
-    feedback_data["labels"].append(label)
-    save_feedback()
-    
-    if len(feedback_data["images"]) % 5 == 0:
-        retrain_model()
-    return "Feedback saved. Thank you!"
-
-def retrain_model():
-    global classifier
-    # Here, include the retraining logic using the feedback_data
-    # This is a placeholder for actual retraining logic
-    print("Retraining the model with new data...")
-
-# Load existing feedback data
-load_feedback()
+import requests
+import torch
+
+# Load the pre-trained model and processor
+processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
+model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')
+
+# Define the prediction function
+def recognize_handwriting(image):
+    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
+
+def provide_feedback(image, correct_text):
+    # Save the feedback to a file or database for later retraining
+    with open("feedback.txt", "a") as f:
+        f.write(f"{correct_text}\n")
+    return "Feedback received. Thank you!"
 
 with gr.Blocks() as demo:
-    with gr.Tab("Predict"):
-        image_input = gr.Sketchpad()
-        output = gr.JSON()
-        image_input.change(fn=predict, inputs=image_input, outputs=output)
+    with gr.Tab("Recognize Handwriting"):
+        image_input = gr.Image(type="pil")
+        output = gr.Textbox(label="Recognized Text")
+        recognize_button = gr.Button("Recognize")
+        recognize_button.click(fn=recognize_handwriting, inputs=image_input, outputs=output)
 
     with gr.Tab("Provide Feedback"):
-        image_feedback = gr.Sketchpad()
-        label_feedback = gr.Textbox(label="Enter the correct label")
+        image_feedback = gr.Image(type="pil")
+        correct_text = gr.Textbox(label="Correct Text")
         feedback_button = gr.Button("Submit Feedback")
         feedback_output = gr.Textbox()
-
-        feedback_button.click(fn=provide_feedback, inputs=[image_feedback, label_feedback], outputs=feedback_output)
+        feedback_button.click(fn=provide_feedback, inputs=[image_feedback, correct_text], outputs=feedback_output)
 
 demo.launch()