Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import os
+import zipfile
+from PIL import Image
+import sympy as sp
+import time
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Page config
+st.set_page_config(page_title="🎓 Smart Math Teacher", layout="centered")
+
+# Custom CSS
+st.markdown("""
+    <style>
+    .fun-title { font-size: 40px; color: #ff3399; text-align: center; font-family: 'Comic Sans MS', cursive; }
+    .question-box { border: 4px dotted #ffcc00; padding: 20px; border-radius: 20px; background-color: #fff7e6; font-size: 20px; }
+    .stButton > button { font-size: 18px; background-color: #00cc99; color: white; border-radius: 10px; padding: 10px; }
+    .stTextInput > div > input { font-size: 18px; }
+    </style>
+""", unsafe_allow_html=True)
+
+# Welcome title
+st.markdown("<div class='fun-title'>🧠✨ Welcome to the Smart Math Teacher! ✨🧠</div>", unsafe_allow_html=True)
+
+# Age group setup
+age_groups = {
+    "4-6 Age Group": {"dataset": "Dataset for 4-6 Age Group.xlsx", "zip_file": "Image_for_group_4-6.zip", "image_folder": "Image_for_group_4-6"},
+    "7-9 Age Group": {"dataset": "Dataset for 7-9 Age Group.xlsx", "zip_file": "Image_for_group_7-9.zip", "image_folder": "Image_for_group_7-9"},
+    "13-15 Age Group": {"dataset": "Dataset for 13-15 Age Group.xlsx", "zip_file": "Image_for_group_13-15.zip", "image_folder": "Image_for_group_13-15"},
+}
+
+selected_age_group = st.selectbox("🧒 Select your Age Group:", list(age_groups.keys()))
+
+# Initialize session
+if "session_initialized" not in st.session_state or st.session_state.age_group != selected_age_group:
+    st.session_state.age_group = selected_age_group
+    st.session_state.category = None
+    st.session_state.question_index = 0
+    st.session_state.show_answer = False
+    st.session_state.show_steps = False
+    st.session_state.session_initialized = True
+
+# Load dataset
+group_info = age_groups[selected_age_group]
+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)):
+    if os.path.exists(zip_path):
+        with zipfile.ZipFile(zip_path, "r") as zip_ref:
+            zip_ref.extractall(image_folder)
+
+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()
+
+# Category selection
+categories = sorted(df['category'].dropna().unique())
+selected_category = st.selectbox("📚 Choose a Math Category:", options=categories)
+
+# Update session if category changes
+if st.session_state.category != selected_category:
+    st.session_state.category = selected_category
+    st.session_state.question_index = 0
+    st.session_state.show_answer = False
+    st.session_state.show_steps = False
+    st.rerun()
+
+# Filter questions by selected category only
+subset_df = df[df['category'] == selected_category].reset_index(drop=True)
+
+if not subset_df.empty and st.session_state.question_index < len(subset_df):
+    question = subset_df.iloc[st.session_state.question_index]
+    progress = int((st.session_state.question_index / len(subset_df)) * 100)
+    st.progress(progress)
+
+    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
+    if pd.notna(question.get('image')):
+        image_name = str(question['image']).strip()
+        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)
+                    except:
+                        st.warning("❌ Image couldn't be loaded.")
+                    break
+
+    user_ans = st.text_input("📝 Your Answer:", key=f"ans_{st.session_state.question_index}")
+
+    if st.button("✅ Submit Answer"):
+        if str(user_ans).strip().lower() == str(question['answer']).strip().lower():
+            st.success("🎉 Correct! Well done!")
+            st.balloons()
+            time.sleep(2)
+            st.session_state.question_index += 1
+            st.session_state.show_answer = False
+            st.session_state.show_steps = False
+            st.rerun()
+        else:
+            st.error("❌ Try again or view the correct answer below.")
+            st.session_state.show_answer = True
+            st.session_state.show_steps = False
+
+    if st.session_state.show_answer:
+        st.info(f"✅ Correct Answer: **{question['answer']}**")
+        if selected_age_group in ["7-9 Age Group", "13-15 Age Group"]:
+            if st.button("🔍 Show Steps"):
+                st.session_state.show_steps = True
+        if st.session_state.show_steps and pd.notna(question.get("steps", None)):
+            st.success(f"### 🪄 Steps:\n{question['steps']}")
+
+    if st.button("⏭️ Skip"):
+        st.session_state.question_index += 1
+        st.session_state.show_answer = False
+        st.session_state.show_steps = False
+        st.rerun()
+elif subset_df.empty:
+    st.warning("⚠️ No questions available in this category. Try another one.")
+else:
+    st.success("🏁 You've completed all questions in this category!")
+
+# -------------------------------------------------------------
+# ✅ Enhanced "Ask Any Math Question" Section Using MiniLM Model
+# -------------------------------------------------------------
+
+@st.cache_resource
+def load_model():
+    return SentenceTransformer("all-MiniLM-L6-v2")
+
+model = load_model()
+
+qa_pairs = {
+    "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",
+    "perimeter of square": "Perimeter = 4 × side",
+    "perimeter of rectangle": "Perimeter = 2 × (length + width)",
+    "volume of cube": "Volume = side³",
+    "volume of sphere": "Volume = (4/3) × π × radius³",
+    "what is pi": "π ≈ 3.14159",
+    "how to calculate speed": "Speed = Distance ÷ Time",
+    "what is the derivative of x squared": "The derivative of x² is 2x."
+}
+
+qa_questions = list(qa_pairs.keys())
+qa_embeddings = model.encode(qa_questions)
+
+st.markdown("## 💡 Ask Any Math Question")
+question_input = st.text_input("Ask a question like 'What is the area of a circle?' or 'Derivative of x^2'")
+
+if question_input:
+    user_embedding = model.encode([question_input])
+    scores = cosine_similarity(user_embedding, qa_embeddings)[0]
+    best_idx = scores.argmax()
+    best_score = scores[best_idx]
+
+    if best_score > 0.6:
+        st.success(f"🤖 Answer:\n\n**{qa_pairs[qa_questions[best_idx]]}**")
+    else:
+        try:
+            if "derivative" in question_input.lower():
+                expr = question_input.lower().split("of")[-1].strip()
+                var = sp.symbols('x')
+                derivative = sp.diff(sp.sympify(expr.replace("^", "**")), var)
+                st.success(f"🧮 Derivative of {expr} is:\n\n**{derivative}**")
+            else:
+                result = sp.sympify(question_input.replace("^", "**"))
+                simplified = sp.simplify(result)
+                st.success(f"✅ Answer: {simplified}")
+        except Exception as e:
+            st.warning(f"⚠️ Couldn't understand the question. Error: {e}")