Streamlit on HF Spaces Intermediate

---
title: My Streamlit App
emoji: 📊
colorFrom: red
colorTo: yellow
sdk: streamlit
sdk_version: 1.37.0
app_file: app.py
pinned: false
---

import streamlit as st
from transformers import pipeline

st.set_page_config(page_title="ML Dashboard", layout="wide")
st.title("ML Model Dashboard")

# Cache the model for performance
@st.cache_resource
def load_model():
    return pipeline("sentiment-analysis")

model = load_model()

# User input
text = st.text_area("Enter text for sentiment analysis:", height=150)

if st.button("Analyze", type="primary"):
    if text:
        result = model(text)
        col1, col2 = st.columns(2)
        with col1:
            st.metric("Sentiment", result[0]["label"])
        with col2:
            st.metric("Confidence", f"{result[0]['score']:.2%}")
    else:
        st.warning("Please enter some text.")

my-streamlit-space/
  app.py                 # Main page
  pages/
    1_Sentiment.py       # Page 1
    2_Summarization.py   # Page 2
    3_Translation.py     # Page 3
  requirements.txt
  README.md

import streamlit as st
from huggingface_hub import InferenceClient

# Use HF Inference API (no model download needed)
client = InferenceClient(token=st.secrets["HF_TOKEN"])

st.title("Chat with an LLM")

prompt = st.chat_input("Ask me anything...")
if prompt:
    with st.chat_message("user"):
        st.write(prompt)
    with st.chat_message("assistant"):
        response = client.text_generation(
            prompt,
            model="mistralai/Mistral-7B-Instruct-v0.2",
            max_new_tokens=500
        )
        st.write(response)