Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,5 +1,71 @@
 
-import numpy as np
-import pandas as pd
+import os
 import streamlit as st
+from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+
+os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.getenv("key")
+os.environ['HF_TOKEN'] = os.getenv("key")
+
+st.set_page_config(page_title="👨‍🏫 Multi-Mentor Chat", page_icon="🧠")
+st.title("🧠 Multi-Topic Mentor")
+
+
+st.markdown("### Select your Mentor:")
+col1, col2, col3, col4, col5, col6 = st.columns(6)
+with col1: st.write("🟢 Python")
+with col2: st.write("🟠 ML")
+with col3: st.write("🔵 DL")
+with col4: st.write("🟣 Stats")
+with col5: st.write("🟡 Data_Anaylasis")
+with col6: st.write("🔴 sql and powerbi")
+
+mentor_type = st.selectbox("Choose a mentor:", ["", "python", "machine_learning", "deep_learning", "stats", "data_anaylasis", "sql and powerbi"])
+
+if mentor_type:
+    st.subheader(f"🧠 {mentor_type.upper()} Mentor Chat")
+    experience = st.slider("Your experience (in years):", 0, 20, 1)
+    user_input = st.text_input("Ask your question:")
+output_container = st.empty()
+
+    if mentor_type == "python":
+        model = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.1-8B-Instruct", provider="nebius", temperature=0.5, max_new_tokens=150, task="conversational")
+    elif mentor_type == "machine_learning":
+        model = HuggingFaceEndpoint(repo_id="deepseek-ai/DeepSeek-R1", provider="nebius", temperature=0.5, max_new_tokens=150, task="conversational")
+    elif mentor_type == "deep_learning":
+        model = HuggingFaceEndpoint(repo_id="deepseek-ai/DeepSeek-R1", provider="nebius", temperature=0.5, max_new_tokens=150, task="conversational")
+    elif mentor_type == "stats":
+        model = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.2-1B-Instruct", provider="nebius", temperature=0.5, max_new_tokens=150, task="conversational")
+    elif mentor_type == "data_anaylasis":
+        model = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.3-70B-Instruct", provider="nebius", temperature=0.5, max_new_tokens=150, task="conversational")
+    elif mentor_type == "sql and powerbi":
+        model = HuggingFaceEndpoint(repo_id="meta-llama/Meta-Llama-3-70B-Instruct", provider="hyperbolic", temperature=0.5, max_new_tokens=150, task="conversational")
+chat_model = ChatHuggingFace(
+        llm=model,
+        repo_id=model.repo_id,
+        provider="nebius",
+        temperature=0.5,
+        max_new_tokens=150,
+        task="conversational"
+    )
+
+
+    if st.button("Ask") and user_input:
+        prompt = ChatPromptTemplate.from_messages([
+            SystemMessagePromptTemplate.from_template(
+                f"You are a helpful and experienced {mentor_type.upper()} mentor assisting a learner with {experience} years of experience."
+            ),
+            HumanMessagePromptTemplate.from_template("{question}")
+        ])
+        formatted_prompt = prompt.format_messages(question=user_input)
+
+        with st.spinner("Mentor is thinking..."):
+            response = chat_model.invoke(formatted_prompt)
+
+        output_container.markdown(f"👤 You: {user_input}")
+        output_container.markdown(f"🧠 Mentor: {response.content}")
+
+    if st.button("Clear Output"):
+        output_container.empty()
+