Code Editor for app.py

import streamlit as st
from preprocess import clean_text, lemmatize
from embedder import get_embedding
from scorer import compute_similarity, calculate_score
from feedback import generate_feedback
from models.model_answer_store import extract_tagged_concepts, concept_score

st.set_page_config(page_title="Automated Theoretical Answer Evaluation System")

st.title("Automated Theoretical Answer Evaluation System")

with st.form("evaluation_form"):
    st.write("Paste the *model answer* (with <primary>, <secondary>, <tertiary> tags):")
    model_ans = st.text_area("Model Answer", height=140)
    st.write("Paste the *student answer*:")
    student_ans = st.text_area("Student Answer", height=140)
    submitted = st.form_submit_button("Evaluate")

if submitted:
    # Preprocess
    clean_student = lemmatize(clean_text(student_ans))
    clean_model = lemmatize(clean_text(model_ans))

    # Embeddings
    try:
        vec_model = get_embedding(clean_model)
        vec_student = get_embedding(clean_student)
        semantic_sim = compute_similarity(vec_model, vec_student)
    except Exception as e:
        st.error(f"Embedding Error: {e}")
        st.stop()

    # Concept Coverage
    concepts = extract_tagged_concepts(model_ans)
    concept_cov = concept_score(clean_student, concepts)

    # Placeholder for structure score
    struct_score = 0.7

    final_score = calculate_score(semantic_sim, concept_cov, struct_score)
    feedback = generate_feedback(final_score, semantic_sim)

    st.subheader("Results")
    st.markdown(f"**Final Score:** {final_score} / 10")
    st.markdown(f"**Semantic Similarity:** {semantic_sim:.4f}")
    st.markdown(f"**Concept Coverage:** {concept_cov:.2f}")
    st.info(feedback)