Create app.py

app.py

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+import streamlit as st
+from huggingface_hub import InferenceClient
+from dotenv import load_dotenv
+import os
+
+# Load .env file
+load_dotenv()
+
+# Get API key from environment variable
+api_key = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+st.set_page_config(page_title="Intellicounsel AI Chat", page_icon="🤖")
+
+# Add system prompt once at the start
+system_prompt = {
+    "role": "system",
+    "content": (
+        "You are Intellicounsel, an intelligent and friendly AI advisor that helps students "
+        "with college applications, SOP reviews, resume tips, and academic advice. "
+        "Respond clearly and helpfully, always tailored to the student's needs."
+    )
+}
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Title
+st.title("🧠 Intellicounsel v2 — AI Chat Advisor")
+
+# User input
+user_input = st.chat_input("Ask something like SOP tips or university suggestions...")
+
+# Show past messages
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+# If new input
+if user_input:
+    st.session_state.messages.append({"role": "user", "content": user_input})
+
+    with st.chat_message("user"):
+        st.markdown(user_input)
+
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            try:
+                client = InferenceClient(
+                    model="nvidia/Llama-3_1-Nemotron-Ultra-253B-v1",
+                    provider="nebius",
+                    api_key=api_key,
+                )
+
+                # Add system prompt only once at the start of the context
+                full_context = [system_prompt] + st.session_state.messages
+
+                completion = client.chat.completions.create(
+                    model="nvidia/Llama-3_1-Nemotron-Ultra-253B-v1",
+                    messages=full_context,
+                    max_tokens=2048,
+                )
+
+                response = completion.choices[0].message.content
+
+            except Exception as e:
+                response = f"❌ Error: {e}"
+
+        st.markdown(response)
+        st.session_state.messages.append({"role": "assistant", "content": response})