Create app.py

app.py

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+import streamlit as st
+from datetime import datetime
+
+# ---------------------------
+# Dummy AI Logic (Data Science Tutor)
+# ---------------------------
+def ds_tutor_response(user_query, chat_history):
+    """
+    Returns AI-like answers to Data Science queries.
+    Simulated without API key.
+    """
+    # Simple keyword-based logic for simulation
+    if "regression" in user_query.lower():
+        return "📊 Regression is a supervised ML technique used to predict continuous values. Example: predicting house prices."
+    elif "classification" in user_query.lower():
+        return "🔍 Classification is used when the target variable is categorical (e.g., spam vs not spam)."
+    elif "neural network" in user_query.lower():
+        return "🧠 A Neural Network is inspired by the human brain. It has layers of interconnected neurons that learn patterns in data."
+    elif "pca" in user_query.lower():
+        return "📉 PCA (Principal Component Analysis) is a dimensionality reduction technique that transforms features into fewer components."
+    elif "accuracy" in user_query.lower():
+        return "✅ Accuracy = (Correct Predictions / Total Predictions). But for imbalanced data, metrics like F1-score are better."
+    else:
+        return "🤔 I'm your Data Science Tutor. Can you clarify your question in Data Science terms?"
+
+# ---------------------------
+# Streamlit UI
+# ---------------------------
+st.set_page_config("AI Data Science Tutor", layout="centered")
+st.title("🎓 AI Conversational Data Science Tutor")
+
+st.write("Ask me any Data Science doubt, and I'll help you understand. Memory is enabled, so I remember our conversation.")
+
+# Session state for chat history
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Chat UI
+for msg in st.session_state.chat_history:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+# User input
+user_input = st.chat_input("Ask your Data Science question...")
+
+if user_input:
+    # Save user message
+    st.session_state.chat_history.append({"role": "user", "content": user_input})
+    with st.chat_message("user"):
+        st.markdown(user_input)
+
+    # Generate AI response
+    ai_reply = ds_tutor_response(user_input, st.session_state.chat_history)
+
+    # Save AI message
+    st.session_state.chat_history.append({"role": "assistant", "content": ai_reply})
+    with st.chat_message("assistant"):
+        st.markdown(ai_reply)