Update app.py

app.py

@@ -1,4 +1,75 @@
 import streamlit as st
+from transformers import pipeline
+import torch
 
-st.title("🚀 Hugging Face + Streamlit via Docker")
-st.write("✅ This app is deployed using Docker on Hugging Face Spaces!")
+st.set_page_config(page_title="Data Science Mentor", layout="wide")
+
+# Cache models to avoid reloading
+@st.cache_resource
+def load_model(topic):
+    # Select model based on topic
+    if topic == "Python":
+        return pipeline("text-generation", model="tiiuae/falcon-7b-instruct", device=0 if torch.cuda.is_available() else -1)
+    elif topic == "GenAI":
+        return pipeline("text2text-generation", model="google/flan-t5-large", device=0 if torch.cuda.is_available() else -1)
+    elif topic == "Statistics":
+        return pipeline("text-generation", model="databricks/dolly-v2-3b", device=0 if torch.cuda.is_available() else -1)
+    elif topic == "SQL":
+        return pipeline("text2text-generation", model="google/flan-t5-base", device=0 if torch.cuda.is_available() else -1)
+    else:
+        # Fallback for Power BI, ML, DL
+        return pipeline("text-generation", model="tiiuae/falcon-7b-instruct", device=0 if torch.cuda.is_available() else -1)
+
+def generate_answer(model, topic, level, question):
+    prompt = f"You are a {level} level mentor in {topic}. Answer the following question in detail:\n{question}"
+
+    if "text-generation" in model.task:
+        output = model(prompt, max_length=256, do_sample=True, top_k=50)
+        answer = output[0]['generated_text']
+    else:
+        output = model(prompt, max_length=256)
+        answer = output[0]['generated_text']
+
+    # Remove prompt from answer if echoed
+    if answer.lower().startswith(prompt.lower()):
+        answer = answer[len(prompt):].strip()
+    return answer
+
+# --- Streamlit UI ---
+
+st.title("🤖 Data Science Mentor")
+
+with st.sidebar:
+    st.header("Configure Your Mentor")
+    topic = st.radio("Select Topic:", ["Python", "GenAI", "Statistics", "Power BI", "SQL", "Machine Learning", "Deep Learning"])
+    level = st.radio("Select Experience Level:", ["Beginner", "Intermediate", "Advanced"])
+
+# Load model for topic
+model = load_model(topic)
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+st.subheader(f"Ask your {topic} question:")
+user_input = st.text_area("Type your question here:", height=100)
+
+if st.button("Get Answer"):
+    if user_input.strip() == "":
+        st.warning("Please enter a question.")
+    else:
+        with st.spinner("Mentor is thinking..."):
+            answer = generate_answer(model, topic, level, user_input)
+            st.session_state.chat_history.append(("You", user_input))
+            st.session_state.chat_history.append(("Mentor", answer))
+
+# Display chat history
+if st.session_state.chat_history:
+    for i in range(0, len(st.session_state.chat_history), 2):
+        user_msg = st.session_state.chat_history[i][1]
+        mentor_msg = st.session_state.chat_history[i+1][1] if i+1 < len(st.session_state.chat_history) else ""
+        st.markdown(f"**You:** {user_msg}")
+        st.markdown(f"**Mentor:** {mentor_msg}")
+        st.markdown("---")
+
+if st.button("Clear Chat"):
+    st.session_state.chat_history = []