Create gen_ai.py

pages/gen_ai.py

@@ -0,0 +1,75 @@
+import streamlit as st
+import os
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
+
+
+
+hf = os.getenv('hf')
+os.environ['HUGGINGFACEHUB_API_TOKEN'] = hf
+os.environ['HF_TOKEN'] = hf
+# --- Config ---
+st.set_page_config(page_title="AI Mentor Chat", layout="centered")
+st.title("🤖 AI Mentor Chat")
+
+# --- Sidebar for selections ---
+st.sidebar.title("Mentor Preferences")
+sub = st.sidebar.text_input("Enter subject:", value="Python")
+exp1 = ['<1', '1', '2', '3', '4', '5', '5+']
+exp = st.sidebar.selectbox("Select experience:", exp1)
+
+# Map experience to label
+experience_map = {
+    '<1': 'New bie mentor',
+    '1': '1', '2': '2', '3': '3', '4': '4', '5': '5',
+    '5+': 'Professional'
+}
+experience_label = experience_map[exp]
+
+# --- Initialize Chat Model ---
+deep_seek_skeleton = HuggingFaceEndpoint(
+    repo_id='google/gemma-2-9b-it',
+    provider='nebius',
+    temperature=0.7,
+    max_new_tokens=150,
+    task='conversational'
+)
+
+deep_seek = ChatHuggingFace(
+    llm=deep_seek_skeleton,
+    repo_id='google/gemma-2-9b-it',
+    provider='nebius',
+    temperature=0.7,
+    max_new_tokens=150,
+    task='conversational'
+)
+
+# --- Session State ---
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# --- Chat Form ---
+with st.form(key="chat_form"):
+    user_input = st.text_input("Ask your question:")
+    submit = st.form_submit_button("Send")
+
+# --- Chat Logic ---
+if submit and user_input:
+    # Add system context
+    system_prompt = f"Act as a microsoft excel mentor who has {experience_label} years of experience who teaches in a very friendly manner and also tells everything in within 150 words. If any question is asked other than microsoft excel  tell politely that this is out of the topic question"
+
+    # Create message list
+    messages = [SystemMessage(content=system_prompt), HumanMessage(content=user_input)]
+
+    # Get model response
+    result = deep_seek.invoke(messages)
+
+    # Append to history
+    st.session_state.chat_history.append((user_input, result.content))
+
+# --- Display Chat History ---
+st.subheader("🗨️ Chat History")
+for i, (user, bot) in enumerate(st.session_state.chat_history):
+    st.markdown(f"**You:** {user}")
+    st.markdown(f"**Mentor:** {bot}")
+    st.markdown("---")