app5.py

import gradio as gr
from PyPDF2 import PdfReader
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer, util
import matplotlib.pyplot as plt
import pandas as pd
import io

# Load transformer model for semantic similarity
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

def extract_text_from_pdf(file_bytes):
    try:
        reader = PdfReader(io.BytesIO(file_bytes))
        return " ".join([page.extract_text() or "" for page in reader.pages]).strip()
    except Exception as e:
        print("Error extracting text:", e)
        return ""

def tfidf_similarity(text1, text2):
    vectorizer = TfidfVectorizer().fit_transform([text1, text2])
    return cosine_similarity(vectorizer[0:1], vectorizer[1:2])[0][0]

def transformer_similarity(text1, text2):
    embeddings = model.encode([text1, text2], convert_to_tensor=True)
    return util.pytorch_cos_sim(embeddings[0], embeddings[1]).item()

def bloom_level(term):
    term = term.lower()
    blooms = {
        "remember": ["define", "list", "recall", "identify"],
        "understand": ["explain", "describe", "summarize"],
        "apply": ["apply", "demonstrate", "use"],
        "analyze": ["analyze", "compare", "contrast"],
        "evaluate": ["evaluate", "judge", "critique"],
        "create": ["create", "design", "formulate"]
    }
    for level, keywords in blooms.items():
        if any(word in term for word in keywords):
            return level.capitalize()
    return "Unknown"

def lo_semantic_scores(los, content):
    scores = []
    for lo in los:
        score = transformer_similarity(lo, content)
        scores.append(score)
    return scores

def compare_all(old_pdf, new_pdf, lo_file):
    try:
        lo_content = lo_file.read().decode("utf-8", errors="ignore") if hasattr(lo_file, "read") else lo_file.decode("utf-8", errors="ignore")
        los = [line.strip() for line in lo_content.splitlines() if line.strip()]
    except Exception as e:
        return "❌ Could not read learning outcomes file.", None, None, None

    old_text = extract_text_from_pdf(old_pdf)
    new_text = extract_text_from_pdf(new_pdf)

    if not old_text or not new_text:
        return "❌ Could not extract text from one or both PDFs.", None, None, None

    tfidf_sim = tfidf_similarity(old_text, new_text)
    transformer_sim = transformer_similarity(old_text, new_text)
    text_growth = round(((len(new_text) - len(old_text)) / len(old_text)) * 100, 2)

    old_scores = lo_semantic_scores(los, old_text)
    new_scores = lo_semantic_scores(los, new_text)

    labels = [f"LO{i+1}" for i in range(len(los))]
    x = range(len(labels))
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.bar(x, old_scores, width=0.4, label="Old", align='center')
    ax.bar([i + 0.4 for i in x], new_scores, width=0.4, label="New", align='center')
    ax.set_xticks([i + 0.2 for i in x])
    ax.set_xticklabels(labels, rotation=45)
    ax.set_ylabel("Semantic Match Score")
    ax.set_title("Learning Outcomes Comparison")
    ax.legend()

    data = {
        "Learning Outcome": labels,
        "LO Text": los,
        "Bloom Level": [bloom_level(lo) for lo in los],
        "Old Match": [round(s*100, 2) for s in old_scores],
        "New Match": [round(s*100, 2) for s in new_scores],
        "Change (%)": [round((n - o)*100, 2) for n, o in zip(new_scores, old_scores)]
    }
    df = pd.DataFrame(data)

    summary = f"""📘 **Summary of Comparison**

📈 **TF-IDF Content Change**: {round((1 - tfidf_sim) * 100, 2)}%
🧠 **Transformer-based Similarity**: {round(transformer_sim * 100, 2)}%
📝 **Content Length Change**: {text_growth}% {"📉 Reduced" if text_growth < 0 else "📈 Increased"}

🎯 **LO Matches**: {sum(1 for score in new_scores if score > 0.5)} of {len(los)}
📊 **Content appears {'more' if sum(new_scores) > sum(old_scores) else 'less'} aligned with learning outcomes.**
"""

    return summary, df, fig, new_text[:2000] + "..."

import gradio as gr

iface = gr.Interface(
    fn=compare_all,
    inputs=[
        gr.File(label="Old Handout PDF", type='binary'),
        gr.File(label="New Handout PDF", type='binary'),
        gr.File(label="Learning Outcomes (TXT)", type='binary')
    ],
    outputs=[
        gr.Markdown(label="📋 Summary"),
        gr.Dataframe(label="📊 LO-wise Comparison Table"),
        gr.Plot(label="📈 LO Match Chart"),
        gr.Textbox(label="📝 Preview of New Content")
    ],
    title="📘 AI Handout Comparator + LO Aligner",
    description="Compare two versions of handouts using both TF-IDF and Transformers. Analyze changes in content, alignment with Learning Outcomes, and Bloom’s taxonomy level."
)

iface.launch()