Update app.py

app.py

@@ -3,10 +3,7 @@ import spacy
 import torch
 import subprocess
 import sys
-import tempfile
-import os
 from transformers import T5ForConditionalGeneration, AutoTokenizer
-from fpdf import FPDF
 
 subprocess.run(
     [sys.executable, "-m", "spacy", "download", "en_core_web_sm"],
@@ -18,24 +15,19 @@ tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
 model     = T5ForConditionalGeneration.from_pretrained(MODEL_ID)
 device    = "cuda" if torch.cuda.is_available() else "cpu"
 model     = model.to(device)
+nlp       = spacy.load("en_core_web_sm")
 
-nlp = spacy.load("en_core_web_sm")
-
-# ── Core generation function ──────────────────────────────────────────
 def generate_questions(passage, num_questions, question_type):
-    if not passage.strip():
-        return "⚠️ Please enter a passage.", None
-
-    doc = nlp(passage)
+    if not passage.strip() or len(passage) < 30:
+        return "⚠️ Please enter a longer passage."
 
-    # Extract candidate answers: named entities first, then noun chunks
+    doc        = nlp(passage)
     candidates = [ent.text for ent in doc.ents]
+
     if len(candidates) < num_questions:
         candidates += [chunk.text for chunk in doc.noun_chunks]
 
-    # Deduplicate and limit
-    seen = set()
-    unique = []
+    seen, unique = set(), []
     for c in candidates:
         if c.lower() not in seen and len(c.split()) <= 5:
             seen.add(c.lower())
@@ -43,9 +35,9 @@ def generate_questions(passage, num_questions, question_type):
     candidates = unique[:num_questions]
 
     if not candidates:
-        return "⚠️ Could not extract answer candidates from this passage. Try a longer or more detailed passage.", None
+        return "⚠️ Could not extract candidates. Try a longer passage."
 
-    output_lines = []
+    lines = []
     for i, answer in enumerate(candidates):
         highlighted = passage.replace(answer, f"<hl> {answer} <hl>", 1)
         input_text  = f"generate question: {highlighted}"
@@ -58,69 +50,40 @@ def generate_questions(passage, num_questions, question_type):
         if question_type == "True / False":
             question = f"True or False: {question.rstrip('?')}?"
 
-        output_lines.append(f"Q{i+1}: {question}")
-        output_lines.append(f"   Answer: {answer}\n")
-
-    return "\n".join(output_lines)
-
-# ── PDF export ────────────────────────────────────────────────────────
-def export_pdf(passage, num_questions, question_type):
-    result = generate_questions(passage, num_questions, question_type)
-    if result.startswith("⚠️"):
-        return None
-
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Helvetica", "B", 14)
-    pdf.cell(0, 10, "Generated Question Paper", ln=True, align="C")
-    pdf.ln(4)
-    pdf.set_font("Helvetica", size=10)
-    pdf.multi_cell(0, 6, f"Passage: {passage[:200]}...")
-    pdf.ln(6)
-    pdf.set_font("Helvetica", size=11)
-    for line in result.split("\n"):
-        pdf.multi_cell(0, 7, line)
-    pdf.ln(4)
-
-    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
-    pdf.output(tmp.name)
-    return tmp.name
-
-# ── Gradio UI ─────────────────────────────────────────────────────────
-with gr.Blocks(title="Automatic Question Generator", theme=gr.themes.Soft()) as demo:
+        lines.append(f"Q{i+1}: {question}")
+        lines.append(f"   Answer: {answer}\n")
+
+    return "\n".join(lines)
 
-    gr.Markdown("""
-    # 🧠 Automatic Question Generator
-    **Fine-tuned T5-small on SQuAD 1.1** · Generates questions from any educational passage
-    """)
+with gr.Blocks(title="Automatic Question Generator", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🧠 Automatic Question Generator\n**Fine-tuned T5-small on SQuAD 1.1**")
 
     with gr.Row():
-        with gr.Column(scale=2):
+        with gr.Column():
             passage_input = gr.Textbox(
                 label="Input Passage",
                 placeholder="Paste any educational paragraph here...",
                 lines=8
             )
-            with gr.Row():
-                num_q = gr.Slider(1, 6, value=3, step=1, label="Number of questions")
-                q_type = gr.Dropdown(
-                    ["Short answer", "True / False"],
-                    value="Short answer",
-                    label="Question type"
-                )
-            with gr.Row():
-                gen_btn = gr.Button("⚡ Generate Questions", variant="primary")
-                pdf_btn = gr.Button("📄 Export PDF")
-
-        with gr.Column(scale=2):
-            output_box = gr.Textbox(label="Generated Questions", lines=16, interactive=False)
-            pdf_output = gr.File(label="Download PDF", visible=True)
-
-    # Sample passages
+            num_q  = gr.Slider(1, 6, value=3, step=1, label="Number of questions")
+            q_type = gr.Dropdown(
+                ["Short answer", "True / False"],
+                value="Short answer",
+                label="Question type"
+            )
+            gen_btn = gr.Button("⚡ Generate Questions", variant="primary")
+
+        with gr.Column():
+            output_box = gr.Textbox(
+                label="Generated Questions",
+                lines=16,
+                interactive=False
+            )
+
     gr.Examples(
         examples=[
             ["Photosynthesis is a process used by plants to convert light energy into chemical energy stored in glucose. It occurs inside chloroplasts using chlorophyll to absorb sunlight.", 3, "Short answer"],
-            ["The Industrial Revolution began in Britain in the late 18th century. The steam engine, invented by James Watt, transformed manufacturing, agriculture, and transportation across the country.", 3, "Short answer"],
+            ["The Industrial Revolution began in Britain in the late 18th century. The steam engine invented by James Watt transformed manufacturing and transportation.", 3, "Short answer"],
             ["Machine learning is a subset of artificial intelligence where systems learn from data to improve performance without being explicitly programmed.", 2, "True / False"],
         ],
         inputs=[passage_input, num_q, q_type]
@@ -131,10 +94,5 @@ with gr.Blocks(title="Automatic Question Generator", theme=gr.themes.Soft()) as
         inputs=[passage_input, num_q, q_type],
         outputs=output_box
     )
-    pdf_btn.click(
-        fn=export_pdf,
-        inputs=[passage_input, num_q, q_type],
-        outputs=pdf_output
-    )
 
-demo.launch(share=False, server_name="0.0.0.0")
+demo.launch(server_name="0.0.0.0", server_port=7860)