Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import pytesseract
+from transformers import pipeline
+import re
+
+# Load pre-trained model for question-answering
+qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+
+# Function to extract text using OCR
+def extract_text_from_image(image_file):
+    image = Image.open(image_file)
+    text = pytesseract.image_to_string(image)
+    return text
+
+# Process text to extract questions
+def extract_questions_from_text(text):
+    # Extract lines that look like questions
+    questions = re.findall(r'(?:[^\n]*\?)', text)
+    return questions
+
+# Extract student name and roll number
+def extract_student_info(text):
+    name = re.search(r"NAME\s*=\s*([\w\s]+)", text, re.IGNORECASE)
+    roll_no = re.search(r"Roll\s*NO\s*=\s*(\d+)", text, re.IGNORECASE)
+    student_name = name.group(1).strip() if name else "Unknown"
+    roll_number = roll_no.group(1).strip() if roll_no else "Unknown"
+    return student_name, roll_number
+
+# Grade answers using QA model
+def grade_answer(question, context):
+    result = qa_pipeline(question=question, context=context)
+    return result['score'], "Correct" if result['score'] > 0.5 else "Incorrect"
+
+# Streamlit App
+st.title("Handwritten Answer Sheet Grading System")
+st.write("Upload an image or handwritten file to process.")
+
+# Upload image
+uploaded_image = st.file_uploader("Upload Handwritten Image", type=["png", "jpg", "jpeg"])
+
+if uploaded_image:
+    st.image(uploaded_image, caption="Uploaded Handwritten File", use_column_width=True)
+
+    # Extract text using OCR
+    extracted_text = extract_text_from_image(uploaded_image)
+    st.subheader("Extracted Text")
+    st.text(extracted_text)
+
+    # Extract student info
+    student_name, roll_number = extract_student_info(extracted_text)
+    st.subheader(f"Student Name: {student_name}")
+    st.subheader(f"Roll No: {roll_number}")
+
+    # Extract questions
+    questions = extract_questions_from_text(extracted_text)
+    st.subheader("Extracted Questions")
+    for i, question in enumerate(questions):
+        st.write(f"Q{i+1}: {question}")
+
+    # Grade the answers
+    st.subheader("Grading Results")
+    for question in questions:
+        score, feedback = grade_answer(question, extracted_text)
+        st.write(f"**Question:** {question}")
+        st.write(f"**Score:** {score:.2f}")
+        st.write(f"**Feedback:** {feedback}")
+        st.write("---")