Update app.py

app.py

@@ -1,106 +1,6 @@
-import streamlit as st
-from langchain.chat_models import ChatHuggingFace
-from langchain.schema import SystemMessage, HumanMessage
-import torch
-from fpdf import FPDF
-import io
-
-st.set_page_config(page_title="Data Science Mentor", layout="wide")
-
-# Cache LangChain models per topic
-@st.cache_resource
-def load_langchain_model(topic):
-    device = 0 if torch.cuda.is_available() else -1
-    if topic == "Python":
-        return ChatHuggingFace(repo_id="tiiuae/falcon-7b-instruct", temperature=0.6, max_new_tokens=256, task="conversational", device=device)
-    elif topic == "GenAI":
-        return ChatHuggingFace(repo_id="google/flan-t5-large", temperature=0.6, max_new_tokens=256, task="conversational", device=device)
-    elif topic == "Statistics":
-        return ChatHuggingFace(repo_id="databricks/dolly-v2-3b", temperature=0.6, max_new_tokens=256, task="conversational", device=device)
-    elif topic == "SQL":
-        return ChatHuggingFace(repo_id="google/flan-t5-base", temperature=0.6, max_new_tokens=256, task="conversational", device=device)
-    else:
-        # Fallback for Power BI, Machine Learning, Deep Learning
-        return ChatHuggingFace(repo_id="tiiuae/falcon-7b-instruct", temperature=0.6, max_new_tokens=256, task="conversational", device=device)
-
-def generate_answer(model, topic, level, question):
-    system_prompt = f"You are a {level} level mentor in {topic}. Answer the user's question accordingly."
-    messages = [
-        SystemMessage(content=system_prompt),
-        HumanMessage(content=question)
-    ]
-    response = model.invoke(messages)
-    return response.content
-
-def create_pdf(chat_history):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_auto_page_break(auto=True, margin=15)
-    pdf.set_font("Arial", size=12)
-
-    pdf.cell(0, 10, "Data Science Mentor Chat History", ln=True, align='C')
-    pdf.ln(10)
-
-    for i in range(0, len(chat_history), 2):
-        user_msg = chat_history[i][1]
-        mentor_msg = chat_history[i+1][1] if i+1 < len(chat_history) else ""
-
-        pdf.set_font("Arial", 'B', 12)
-        pdf.multi_cell(0, 10, f"You: {user_msg}")
-        pdf.set_font("Arial", '', 12)
-        pdf.multi_cell(0, 10, f"Mentor: {mentor_msg}")
-        pdf.ln(5)
-
-    pdf_buffer = io.BytesIO()
-    pdf.output(pdf_buffer)
-    pdf_buffer.seek(0)
-    return pdf_buffer
-
-# --- 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 LangChain model for selected topic
-model = load_langchain_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("---")
-
-    # PDF Download Button
-    if st.button("Download Chat as PDF"):
-        pdf_bytes = create_pdf(st.session_state.chat_history)
-        st.download_button(
-            label="Download PDF",
-            data=pdf_bytes,
-            file_name="chat_history.pdf",
-            mime="application/pdf"
-        )
-
-if st.button("Clear Chat"):
-    st.session_state.chat_history = []
+import os
+import langchain
+import langchain_huggingface
+from langchain_huggingface import HuggingFaceEndpoint,HuggingFacePipeline, ChatHuggingFace
+from langchain_google_genai import GoogleGenerativeAI, ChatGoogleGenerativeAI
+from langchain_core.messages import HumanMessage, SystemMessage, AIMessage