Update app.py

app.py

@@ -9,17 +9,14 @@ import os
 
 print("Installation complete. Loading models...")
 
-# Load models once at startup
 model_name = "csebuetnlp/mT5_multilingual_XLSum"
 tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-# If you have a GPU, use it
 device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"Using device: {device}")
 model = model.to(device)
 
-# Load question generator once
 question_generator = pipeline(
     "text2text-generation",
     model="valhalla/t5-small-e2e-qg",
@@ -43,9 +40,8 @@ def summarize_text(text, src_lang):
     return summary
 
 def generate_questions(summary):
-    # Generate questions one at a time with beam search
     questions = []
-    for _ in range(3):  # Generate 3 questions
+    for _ in range(3): 
         result = question_generator(
             summary,
             max_length=64,
@@ -57,39 +53,36 @@ def generate_questions(summary):
         )
         questions.append(result[0]['generated_text'])
 
-    # Remove duplicates
     questions = list(set(questions))
     return questions
 
 def generate_concept_map(summary, questions):
-    # Use NetworkX and matplotlib for rendering
+   
     G = nx.DiGraph()
 
-    # Add summary as central node
+    
     summary_short = summary[:50] + "..." if len(summary) > 50 else summary
     G.add_node("summary", label=summary_short)
 
-    # Add question nodes and edges
+    
     for i, question in enumerate(questions):
         q_short = question[:30] + "..." if len(question) > 30 else question
         node_id = f"Q{i}"
         G.add_node(node_id, label=q_short)
         G.add_edge("summary", node_id)
 
-    # Create the plot directly in memory
+    
     plt.figure(figsize=(10, 8))
-    pos = nx.spring_layout(G, seed=42)  # Fixed seed for consistent layout
+    pos = nx.spring_layout(G, seed=42)  
     nx.draw(G, pos, with_labels=False, node_color='skyblue',
             node_size=1500, arrows=True, connectionstyle='arc3,rad=0.1',
             edgecolors='black', linewidths=1)
 
-    # Add labels with better font handling
-    # FIX: Removed 'wrap' parameter which is not supported in this version of NetworkX
+    
     labels = nx.get_node_attributes(G, 'label')
     nx.draw_networkx_labels(G, pos, labels=labels, font_size=9,
                          font_family='sans-serif')
 
-    # Save to memory buffer
     buf = io.BytesIO()
     plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
     buf.seek(0)
@@ -101,7 +94,7 @@ def analyze_text(text, lang):
     if not text.strip():
         return "Please enter some text.", "No questions generated.", None
 
-    # Process the text
+   
     try:
         print("Generating summary...")
         summary = summarize_text(text, lang)
@@ -122,40 +115,32 @@ def analyze_text(text, lang):
         print(traceback.format_exc())
         return f"Error processing text: {str(e)}", "", None
 
-# Alternative simpler concept map function in case the above still has issues
 def generate_simple_concept_map(summary, questions):
     """Fallback concept map generator with minimal dependencies"""
     plt.figure(figsize=(10, 8))
 
-    # Create a simple radial layout
     n_questions = len(questions)
 
-    # Draw the central node (summary)
     plt.scatter([0], [0], s=1000, color='skyblue', edgecolors='black')
     plt.text(0, 0, summary[:50] + "..." if len(summary) > 50 else summary,
              ha='center', va='center', fontsize=9)
 
-    # Draw the question nodes in a circle around the summary
     radius = 5
     for i, question in enumerate(questions):
         angle = 2 * 3.14159 * i / max(n_questions, 1)
         x = radius * 0.8 * -1 * (max(n_questions, 1) - 1) * ((i / max(n_questions - 1, 1)) - 0.5)
         y = radius * 0.6 * (i % 2 * 2 - 1)
 
-        # Draw node
         plt.scatter([x], [y], s=800, color='lightgreen', edgecolors='black')
 
-        # Draw edge from summary to question
         plt.plot([0, x], [0, y], 'k-', alpha=0.6)
 
-        # Add question text
         plt.text(x, y, question[:30] + "..." if len(question) > 30 else question,
                 ha='center', va='center', fontsize=8)
 
     plt.axis('equal')
     plt.axis('off')
 
-    # Save to memory buffer
     buf = io.BytesIO()
     plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
     buf.seek(0)
@@ -184,14 +169,11 @@ def analyze_text_with_fallback(text, lang):
 
         print("Creating concept map...")
         try:
-            # Try the main concept map generator first
             concept_map_image = generate_concept_map(summary, questions)
         except Exception as e:
             print(f"Main concept map failed: {e}, using fallback")
-            # If it fails, use the fallback generator
             concept_map_image = generate_simple_concept_map(summary, questions)
 
-        # Format questions as a list
         questions_text = "\n".join([f"- {q}" for q in questions])
 
         return summary, questions_text, concept_map_image
@@ -202,7 +184,7 @@ def analyze_text_with_fallback(text, lang):
         return f"Error processing text: {str(e)}", "", None
 
 iface = gr.Interface(
-    fn=analyze_text_with_fallback,  # Use the function with fallback
+    fn=analyze_text_with_fallback, 
     inputs=[gr.Textbox(lines=10, placeholder="Enter text here..."), gr.Dropdown(["ar", "en"], label="Language")],
     outputs=[gr.Textbox(label="Summary"), gr.Textbox(label="Questions"), gr.Image(label="Concept Map")],
     examples=examples,
@@ -210,5 +192,4 @@ iface = gr.Interface(
     description="Enter a text in Arabic or English and the model will summarize it and generate questions and a concept map."
 )
 
-# For Colab, we need to use a public URL
 iface.launch(share=True)