Update src/streamlit_app.py

src/streamlit_app.py

@@ -8,6 +8,9 @@ import sympy as sp
 import time
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
+import tempfile
+import pickle
+import pickletools
 
 # Page config
 st.set_page_config(page_title="🎓 Smart Math Teacher", layout="centered")
@@ -25,6 +28,10 @@ st.markdown("""
 # Welcome title
 st.markdown("<div class='fun-title'>🧠✨ Welcome to the Smart Math Teacher! ✨🧠</div>", unsafe_allow_html=True)
 
+# Initialize session state for temp directory
+if 'temp_dir' not in st.session_state:
+    st.session_state.temp_dir = tempfile.mkdtemp()
+
 # Age group setup 
 age_groups = {
     "4-6 Age Group": {"dataset": "Dataset%20for%204-6%20Age%20Group.xlsx", "zip_file": "Image_for_group_4-6.zip", "image_folder": "Image_for_group_4-6"},
@@ -49,18 +56,33 @@ dataset_path = group_info["dataset"]
 zip_path = group_info["zip_file"]
 image_folder = group_info["image_folder"]
 
-os.makedirs(image_folder, exist_ok=True)
-if not any(f.lower().endswith(('.png', '.jpg', '.jpeg')) for f in os.listdir(image_folder)):
+# Create image folder in temp directory
+temp_image_folder = os.path.join(st.session_state.temp_dir, image_folder)
+os.makedirs(temp_image_folder, exist_ok=True)
+
+# Extract images if zip exists
+try:
     if os.path.exists(zip_path):
         with zipfile.ZipFile(zip_path, "r") as zip_ref:
-            zip_ref.extractall(image_folder)
+            zip_ref.extractall(temp_image_folder)
+    else:
+        st.warning(f"Zip file not found: {zip_path}. Using default images if available.")
+except PermissionError as e:
+    st.error(f"Permission error accessing files: {e}")
+    st.info("Using default images without extraction.")
+except Exception as e:
+    st.error(f"Error extracting images: {e}")
 
 if not os.path.exists(dataset_path):
     st.error(f"Dataset not found: {dataset_path}")
     st.stop()
 
-df = pd.read_excel(dataset_path)
-df['category'] = df['category'].astype(str).str.strip()
+try:
+    df = pd.read_excel(dataset_path)
+    df['category'] = df['category'].astype(str).str.strip()
+except Exception as e:
+    st.error(f"Error loading dataset: {e}")
+    st.stop()
 
 # Category selection
 categories = sorted(df['category'].dropna().unique())
@@ -84,17 +106,37 @@ if not subset_df.empty and st.session_state.question_index < len(subset_df):
 
     st.markdown(f"<div class='question-box'>📘 <b>Question {st.session_state.question_index + 1}:</b><br><br>{question['problem']}</div>", unsafe_allow_html=True)
 
-    # Display image
+    # Display image with error handling
     if pd.notna(question.get('image')):
         image_name = str(question['image']).strip()
-        for root, _, files in os.walk(image_folder):
+        image_found = False
+        
+        # Check in temp directory first
+        for root, _, files in os.walk(temp_image_folder):
             for file in files:
                 if file.lower().startswith(image_name.lower()) or os.path.splitext(file)[0].lower() == image_name.lower():
                     try:
-                        st.image(Image.open(os.path.join(root, file)), use_column_width=True)
-                    except:
-                        st.warning("❌ Image couldn't be loaded.")
-                    break
+                        img_path = os.path.join(root, file)
+                        st.image(Image.open(img_path), use_column_width=True)
+                        image_found = True
+                        break
+                    except Exception as e:
+                        st.warning(f"❌ Couldn't load image: {e}")
+        
+        if not image_found:
+            # Fallback to original image folder
+            for root, _, files in os.walk(image_folder):
+                for file in files:
+                    if file.lower().startswith(image_name.lower()) or os.path.splitext(file)[0].lower() == image_name.lower():
+                        try:
+                            st.image(Image.open(os.path.join(root, file)), use_column_width=True)
+                            image_found = True
+                            break
+                        except Exception as e:
+                            st.warning(f"❌ Couldn't load image: {e}")
+        
+        if not image_found:
+            st.warning("❌ Image not found.")
 
     user_ans = st.text_input("📝 Your Answer:", key=f"ans_{st.session_state.question_index}")
 
@@ -140,7 +182,8 @@ def load_model():
 
 model = load_model()
 
-qa_pairs = {
+# Use pickle to store and share QA data efficiently
+qa_data = {
     "what is the area of a triangle": "Area = (1/2) × base × height",
     "what is the area of a square": "Area = side²",
     "what is the area of a circle": "Area = π × radius²",
@@ -153,7 +196,18 @@ qa_pairs = {
     "what is the derivative of x squared": "The derivative of x² is 2x."
 }
 
-qa_questions = list(qa_pairs.keys())
+# Store QA data using pickle (for demonstration)
+try:
+    with open('qa_data.pkl', 'wb') as f:
+        pickle.dump(qa_data, f)
+    
+    # Optional: disassemble and analyze pickle (for debugging)
+    with open('qa_data.pkl', 'rb') as f:
+        pickletools.dis(f)
+except Exception as e:
+    st.warning(f"Could not create pickle file: {e}")
+
+qa_questions = list(qa_data.keys())
 qa_embeddings = model.encode(qa_questions)
 
 st.markdown("## 💡 Ask Any Math Question")
@@ -166,7 +220,7 @@ if question_input:
     best_score = scores[best_idx]
 
     if best_score > 0.6:
-        st.success(f"🤖 Answer:\n\n**{qa_pairs[qa_questions[best_idx]]}**")
+        st.success(f"🤖 Answer:\n\n**{qa_data[qa_questions[best_idx]]}**")
     else:
         try:
             if "derivative" in question_input.lower():
@@ -180,3 +234,13 @@ if question_input:
                 st.success(f"✅ Answer: {simplified}")
         except Exception as e:
             st.warning(f"⚠️ Couldn't understand the question. Error: {e}")
+
+# Cleanup function (optional)
+def cleanup():
+    import shutil
+    if 'temp_dir' in st.session_state and os.path.exists(st.session_state.temp_dir):
+        shutil.rmtree(st.session_state.temp_dir)
+
+# Register cleanup when app closes
+import atexit
+atexit.register(cleanup)