Create app.py

app.py

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+import gradio as gr
+import fitz  # PyMuPDF
+import docx
+import io
+import re
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sentence_transformers import SentenceTransformer, util
+import matplotlib.pyplot as plt
+import numpy as np
+from difflib import SequenceMatcher
+
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+def extract_text_from_pdf(pdf_file):
+    try:
+        pdf_reader = fitz.open(stream=pdf_file, filetype="pdf")
+        text = ""
+        for page in pdf_reader:
+            text += page.get_text()
+        pdf_reader.close()
+        return text.strip()
+    except Exception as e:
+        return ""
+
+def normalize_text(text):
+    return re.sub(r'\s+', ' ', text.strip().lower())
+
+def extract_text_from_docx(docx_file):
+    try:
+        doc = docx.Document(io.BytesIO(docx_file))
+        full_text = []
+        for para in doc.paragraphs:
+            if para.text.strip():
+                full_text.append(para.text.strip())
+        return full_text
+    except:
+        return []
+
+def semantic_match(lo_list, content):
+    scores = []
+    for lo in lo_list:
+        try:
+            lo_embed = model.encode(lo, convert_to_tensor=True)
+            content_embed = model.encode(content, convert_to_tensor=True)
+            sim = util.pytorch_cos_sim(lo_embed, content_embed).item()
+            scores.append(round(sim, 2))
+        except:
+            scores.append(0.0)
+    return scores
+
+def content_change_score(text1, text2):
+    try:
+        sim = SequenceMatcher(None, normalize_text(text1), normalize_text(text2)).ratio()
+        return round((1 - sim) * 100, 2)
+    except:
+        return 100.0
+
+def compare_handouts(old_pdf, new_pdf, lo_file):
+    old_text = extract_text_from_pdf(old_pdf)
+    new_text = extract_text_from_pdf(new_pdf)
+
+    if len(old_text.strip()) < 200 or len(new_text.strip()) < 200:
+        return "⚠️ Could not extract meaningful content from one or both PDFs.", None
+
+    lo_list = extract_text_from_docx(lo_file)
+    if not lo_list:
+        return "⚠️ No learning outcomes detected.", None
+
+    old_scores = semantic_match(lo_list, old_text)
+    new_scores = semantic_match(lo_list, new_text)
+
+    change_percent = content_change_score(old_text, new_text)
+    improved_count = sum([n > o for n, o in zip(new_scores, old_scores)])
+    matched_los = sum([n >= o for n, o in zip(new_scores, old_scores)])
+
+    summary = f"📈 Content Change Estimate: {change_percent}%\n"
+    summary += f"🧠 LO Alignment: {matched_los} of {len(lo_list)} learning outcomes matched\n"
+    if improved_count > 0:
+        summary += "🟢 Summary: New handout has improved structure and added clarity."
+    else:
+        summary += "⚠️ Summary: No significant improvement in LO alignment."
+
+    # Plot
+    x = np.arange(len(lo_list))
+    width = 0.35
+    fig, ax = plt.subplots()
+    ax.bar(x - width/2, old_scores, width, label='Old')
+    ax.bar(x + width/2, new_scores, width, label='New')
+    ax.set_ylabel('Match Score (0-1)')
+    ax.set_title('LO-wise Match Score: Old vs New')
+    ax.set_xticks(x)
+    ax.set_xticklabels([f"LO{i+1}" for i in range(len(lo_list))], rotation=45)
+    ax.legend()
+    plt.tight_layout()
+
+    return summary, fig
+
+with gr.Blocks() as demo:
+    gr.Markdown("📘 **Educational Content Comparator**")
+    gr.Markdown("Upload 2 handouts and a .docx file of Learning Outcomes to compare changes and alignment.")
+
+    with gr.Row():
+        old_pdf = gr.File(label="📂 Upload Old PDF", file_types=[".pdf"], type="binary")
+        new_pdf = gr.File(label="📂 Upload New PDF", file_types=[".pdf"], type="binary")
+        lo_file = gr.File(label="📂 Upload Learning Outcomes (.docx)", file_types=[".docx"], type="binary")
+
+    with gr.Row():
+        btn = gr.Button("Submit")
+        clear_btn = gr.Button("Clear")
+
+    output_text = gr.Textbox(label="📋 Summary", lines=5, interactive=False)
+    output_plot = gr.Plot(label="📊 LO Match Chart")
+
+    btn.click(fn=compare_handouts, inputs=[old_pdf, new_pdf, lo_file], outputs=[output_text, output_plot])
+    clear_btn.click(fn=lambda: ("", None), inputs=[], outputs=[output_text, output_plot])
+
+demo.launch()