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| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| # Load FREE models | |
| ocr_pipe = pipeline("image-to-text", model="microsoft/trocr-base-handwritten") | |
| similarity_pipe = pipeline("feature-extraction", model="sentence-transformers/all-MiniLM-L6-v2") | |
| def validate_answer(image, user_text, correct_answer): | |
| # OCR for handwritten text | |
| if image: | |
| ocr_result = ocr_pipe(image) | |
| user_text = ocr_result[0]['generated_text'] | |
| # Check clarity (rule-based) | |
| clarity = sum(c.isalnum() for c in user_text) / max(1, len(user_text)) | |
| if clarity < 0.7: | |
| return "⚠️ Handwriting unclear", "", "" | |
| # Semantic comparison | |
| embeddings = similarity_pipe([correct_answer, user_text]) | |
| similarity = np.dot(embeddings[0], embeddings[1]) | |
| return ( | |
| f"✅ Clarity: {clarity:.0%}", | |
| f"📝 Extracted: {user_text}", | |
| f"🔍 Similarity: {similarity:.0%}" | |
| ) | |
| # Create interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Free Answer Validator") | |
| with gr.Row(): | |
| image_input = gr.Image(label="Upload Handwritten Answer", type="pil") | |
| text_input = gr.Textbox(label="Or Type Answer Here") | |
| correct_input = gr.Textbox(label="Correct Answer", value="The Earth revolves around the Sun.") | |
| submit_btn = gr.Button("Validate") | |
| clarity_out = gr.Textbox(label="Clarity Check") | |
| extracted_out = gr.Textbox(label="Extracted Text") | |
| similarity_out = gr.Textbox(label="Similarity Score") | |
| submit_btn.click( | |
| validate_answer, | |
| inputs=[image_input, text_input, correct_input], | |
| outputs=[clarity_out, extracted_out, similarity_out] | |
| ) | |
| demo.launch() |