Create app.py

app.py

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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()