Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import random
+from paddleocr import PaddleOCR
+
+# Initialize PaddleOCR reader once (use English model here)
+ocr = PaddleOCR(use_angle_cls=True, lang='en', use_gpu=False)
+
+def ocr_paddle_with_random_scores(img, correct_text):
+    if img is None:
+        return "No image uploaded", "", ""
+
+    # Convert PIL image to numpy array
+    img_array = np.array(img)
+
+    try:
+        # Perform OCR, get list of results
+        results = ocr.ocr(img_array, cls=True)
+
+        # Extract text lines from results
+        detected_text_lines = [line[1][0] for line in results]
+        detected_text = "\n".join(detected_text_lines)
+
+        # Generate random accuracy and pipeline scores between 80% and 85%
+        accuracy = random.uniform(0.80, 0.85)
+        pipeline_score = random.uniform(0.80, 0.85)
+
+        accuracy_str = f"{accuracy:.2%}"
+        pipeline_score_str = f"{pipeline_score:.2%}"
+
+        return detected_text, accuracy_str, pipeline_score_str
+
+    except Exception as e:
+        error_msg = f"PaddleOCR Error: {str(e)}"
+        return error_msg, "", ""
+
+with gr.Blocks() as demo:
+    gr.Markdown("# PaddleOCR Demo with Lower Randomized Accuracy & Pipeline Scores")
+
+    with gr.Row():
+        img_input = gr.Image(type="pil", label="Upload Image")
+        correct_text_input = gr.Textbox(label="Enter Correct Text (for display only)", lines=4)
+
+    output_text = gr.Textbox(label="OCR Result", lines=10)
+    accuracy_output = gr.Textbox(label="Accuracy (Randomized)", interactive=False)
+    pipeline_output = gr.Textbox(label="Pipeline Integration Score (Randomized)", interactive=False)
+
+    run_button = gr.Button("Run OCR")
+    run_button.click(
+        ocr_paddle_with_random_scores,
+        inputs=[img_input, correct_text_input],
+        outputs=[output_text, accuracy_output, pipeline_output]
+    )
+
+demo.launch()