Update app.py

app.py

@@ -4,11 +4,11 @@ import fitz  # PyMuPDF
 import docx
 import io
 import re
-import pandas as pd
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sentence_transformers import SentenceTransformer, util
+import os
 import matplotlib.pyplot as plt
 import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sentence_transformers import SentenceTransformer, util
 from difflib import SequenceMatcher
 
 model = SentenceTransformer('all-MiniLM-L6-v2')
@@ -21,7 +21,7 @@ def extract_text_from_pdf(pdf_file):
             text += page.get_text()
         pdf_reader.close()
         return text.strip()
-    except Exception:
+    except Exception as e:
         return ""
 
 def normalize_text(text):
@@ -30,17 +30,24 @@ def normalize_text(text):
 def extract_text_from_docx(docx_file):
     try:
         doc = docx.Document(io.BytesIO(docx_file))
-        return [p.text.strip() for p in doc.paragraphs if p.text.strip()]
+        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 = []
-    content_embed = model.encode(content, convert_to_tensor=True)
     for lo in lo_list:
-        lo_embed = model.encode(lo, convert_to_tensor=True)
-        sim = util.pytorch_cos_sim(lo_embed, content_embed).item()
-        scores.append(round(sim, 3))
+        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):
@@ -55,78 +62,58 @@ def compare_handouts(old_pdf, new_pdf, lo_file):
     new_text = extract_text_from_pdf(new_pdf)
 
     if len(old_text.strip()) < 200 or len(new_text.strip()) < 200:
-        return "❌ Could not extract text from one or both PDFs.", None, None
+        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, None
+        return "⚠️ No learning outcomes detected.", None
 
     old_scores = semantic_match(lo_list, old_text)
     new_scores = semantic_match(lo_list, new_text)
-    improvement = [round(n - o, 3) for n, o in zip(new_scores, old_scores)]
-    improved_count = sum([i > 0 for i in improvement])
-    
-    # Prepare Excel output
-    df = pd.DataFrame({
-        "Learning Outcome": lo_list,
-        "Old Match Score": old_scores,
-        "New Match Score": new_scores,
-        "Improvement": improvement
-    })
-
-    excel_path = "/mnt/data/LO_Comparison_Report.xlsx"
-    df.to_excel(excel_path, index=False)
-
-    # Scores
-    content_diff = content_change_score(old_text, new_text)
-    lo_improvement_percent = round((sum(improvement) / len(lo_list)) * 100, 2)
-
-    summary = (
-        f"🧠 Improved LOs: {improved_count} / {len(lo_list)}\n"
-        f"📄 Content Change Estimate: {content_diff}%\n"
-        f"📊 Avg LO Improvement Score: {lo_improvement_percent}%\n\n"
-    )
 
+    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 better aligns with LOs and has improved clarity."
+        summary += "🟢 Summary: New handout has improved structure and added clarity."
     else:
         summary += "⚠️ Summary: No significant improvement in LO alignment."
 
-    # Bar chart
+    # Plot
     x = np.arange(len(lo_list))
     width = 0.35
-    fig, ax = plt.subplots(figsize=(10, 5))
+    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 Comparison')
+    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, excel_path
+    return summary, fig
 
 with gr.Blocks() as demo:
-    gr.Markdown("### 📘 Educational Handout Comparison Tool")
-    gr.Markdown("Upload an old and new handout PDF, along with Learning Outcomes (.docx), to compare updates.")
+    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="📂 Old Handout", file_types=[".pdf"], type="binary")
-        new_pdf = gr.File(label="📂 New Handout", file_types=[".pdf"], type="binary")
-        lo_file = gr.File(label="📂 Learning Outcomes (.docx)", file_types=[".docx"], type="binary")
+        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("🔍 Compare")
-        clear_btn = gr.Button("♻️ Clear")
+        btn = gr.Button("Submit")
+        clear_btn = gr.Button("Clear")
 
-    summary_out = gr.Textbox(label="📋 Summary", lines=6, interactive=False)
-    plot_out = gr.Plot(label="📊 LO Score Chart")
-    download_link = gr.File(label="📥 Download Excel Report")
+    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=[summary_out, plot_out, download_link])
-    clear_btn.click(fn=lambda: ("", None, None),
-                    inputs=[], outputs=[summary_out, plot_out, download_link])
+    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()
+demo.launch()