app.py

# streamlit_app.py
import os
import streamlit as st
from huggingface_hub import InferenceClient
from dotenv import load_dotenv

# Load environment variables for local development
load_dotenv()

# Initialize the Hugging Face Inference client
hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
client = InferenceClient(provider="auto", api_key=hf_token)

# Streamlit configuration
st.set_page_config(
    page_title="Interview Prep Bot",
    page_icon="🧠",
    layout="centered"
)

st.title("🎓 Interview Preparation Chatbot")

# Session state for conversation history and selected topic
if "history" not in st.session_state:
    st.session_state.history = []
if "topic" not in st.session_state:
    st.session_state.topic = "Machine Learning"

# Sidebar: Topic selection
st.sidebar.header("Practice Topic")
st.session_state.topic = st.sidebar.selectbox(
    "Select a topic:",
    [
        "Machine Learning",
        "Data Structures",
        "Python",
        "Generative AI",
        "Computer Vision",
        "Deep Learning",
    ],
    index=["Machine Learning","Data Structures","Python","Generative AI","Computer Vision","Deep Learning"].index(st.session_state.topic)
)

# Display existing chat history
for sender, message in st.session_state.history:
    role = "user" if sender == "You" else "assistant"
    st.chat_message(role).write(message)

# User input
user_input = st.chat_input("Ask me anything about " + st.session_state.topic + "…")

if user_input:
    # Append user message
    st.session_state.history.append(("You", user_input))
    st.chat_message("user").write(user_input)

    # Placeholder for bot response
    placeholder = st.chat_message("assistant")
    placeholder.write("⏳ Thinking...")

    # Build messages for the API call
    messages = []
    for role, text in [("user", msg) if s == "You" else ("assistant", msg)
                       for s, msg in st.session_state.history]:
        messages.append({"role": role, "content": text})

    # Call the Inference API
    try:
        response = client.chat.completions.create(
            model="mistralai/Mistral-7B-Instruct-v0.1",
            messages=messages
        )
        bot_reply = response.choices[0].message["content"].strip()
    except Exception as e:
        bot_reply = f"❌ API Error: {e}"

    # Display and store bot reply
    placeholder.write(bot_reply)
    st.session_state.history.append(("Bot", bot_reply))

```