Update app.py

app.py

@@ -1,58 +1,100 @@
 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?"
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import AIMessage, HumanMessage
+from langchain.memory import ConversationBufferMemory
+
+# ---------------------------
+# Streamlit UI Config
+# ---------------------------
+st.set_page_config(page_title="AI Data Science Tutor", layout="wide")
+st.title("📊 AI Conversational Data Science Tutor")
 
 # ---------------------------
-# Streamlit UI
+# Sidebar - Settings
 # ---------------------------
-st.set_page_config("AI Data Science Tutor", layout="centered")
-st.title("🎓 AI Conversational Data Science Tutor")
+st.sidebar.header("⚙️ Settings")
 
-st.write("Ask me any Data Science doubt, and I'll help you understand. Memory is enabled, so I remember our conversation.")
+mode = st.sidebar.radio(
+    "Choose Tutor Mode:",
+    ("Dummy Tutor (No API Key)", "OpenAI Tutor (API Key Required)")
+)
+
+if mode == "OpenAI Tutor (API Key Required)":
+    openai_api_key = st.sidebar.text_input("Enter your OpenAI API Key:", type="password")
+else:
+    openai_api_key = None
+
+# ---------------------------
+# Initialize Memory
+# ---------------------------
+if "memory" not in st.session_state:
+    st.session_state.memory = ConversationBufferMemory(return_messages=True)
 
-# Session state for chat history
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = []
 
+# ---------------------------
+# Dummy Tutor Response Logic
+# ---------------------------
+def dummy_tutor_response(user_query):
+    q = user_query.lower()
+    if "regression" in q:
+        return "📊 Regression is a supervised ML technique used to predict continuous values."
+    elif "classification" in q:
+        return "🔍 Classification predicts categorical labels, e.g., spam vs not spam."
+    elif "neural" in q or "deep learning" in q:
+        return "🧠 Neural Networks consist of layers of neurons that learn patterns from data."
+    elif "pca" in q:
+        return "📉 PCA reduces dimensions while preserving variance."
+    elif "accuracy" in q or "precision" in q or "recall" in q:
+        return "✅ Accuracy = correct predictions / total. For imbalanced data, use precision, recall, or F1-score."
+    elif "clustering" in q:
+        return "📌 Clustering groups similar points without labels (unsupervised learning)."
+    elif "overfitting" in q:
+        return "⚠️ Overfitting means the model memorizes data instead of generalizing."
+    else:
+        return f"🤔 I didn’t fully get that. Can you rephrase your Data Science question? (You asked: {user_query})"
+
+# ---------------------------
+# Get AI Tutor Response
+# ---------------------------
+def get_tutor_response(user_query):
+    if mode == "Dummy Tutor (No API Key)":
+        return dummy_tutor_response(user_query)
+
+    if mode == "OpenAI Tutor (API Key Required)" and openai_api_key:
+        llm = ChatOpenAI(
+            model="gpt-4o-mini",  # You can also try "gpt-4o" or "gpt-4"
+            openai_api_key=openai_api_key,
+            temperature=0.5
+        )
+
+        # Add user message to memory
+        st.session_state.memory.chat_memory.add_user_message(user_query)
+
+        # Generate response
+        response = llm(st.session_state.memory.chat_memory.messages)
+
+        # Add AI message to memory
+        st.session_state.memory.chat_memory.add_ai_message(response.content)
+
+        return response.content
+
+    return "⚠️ Please provide your OpenAI API key in the sidebar."
+
+# ---------------------------
 # 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)
+# ---------------------------
+user_query = st.chat_input("Ask me a Data Science question...")
+
+if user_query:
+    response = get_tutor_response(user_query)
+    st.session_state.chat_history.append(("You", user_query))
+    st.session_state.chat_history.append(("Tutor", response))
+
+# Display Chat History
+for sender, msg in st.session_state.chat_history:
+    if sender == "You":
+        st.markdown(f"**👩‍💻 {sender}:** {msg}")
+    else:
+        st.markdown(f"**🤖 {sender}:** {msg}")