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Streamlit app for CV200 Pet Classifier
Run:
streamlit run streamlit_app.py
"""
import os
import streamlit as st
import requests
from PIL import Image
import io
# Streamlit Community Cloud stores secrets in `st.secrets` (not necessarily env vars).
def _get_api_url_default() -> str:
try:
secret_val = st.secrets.get("API_URL") # type: ignore[attr-defined]
except Exception:
secret_val = None
return secret_val or os.environ.get("API_URL") or "https://solarevat-cv200.hf.space"
st.set_page_config(
page_title="Pet Classifier",
page_icon="🐾",
layout="wide",
initial_sidebar_state="expanded",
)
# Get API URL from secrets/env
if "api_url" not in st.session_state:
st.session_state.api_url = _get_api_url_default()
st.title("🐾 Pet Classifier")
st.markdown("Upload an image of a pet to classify it using our CV200 model!")
# Sidebar with info and settings
with st.sidebar:
st.header("Settings")
st.session_state.api_url = st.text_input(
"API_URL",
value=st.session_state.api_url,
help="FastAPI base URL. Default: https://solarevat-cv200.hf.space",
)
st.header("About")
st.markdown("""
This app uses a deep learning model trained on 200 pet classes.
**How to use:**
1. Upload an image of a pet
2. Click "Classify Pet"
3. See the top 5 predictions with confidence scores
""")
st.header("API Info")
st.markdown("**Endpoints:**")
st.markdown("- `/predict` - Single image")
st.markdown("- `/predict_batch` - Multiple images")
st.markdown("- `/docs` - API documentation")
st.markdown("- `/healthz` - Health check")
st.info(
f"Current API_URL: `{st.session_state.api_url}` — [View API docs]({st.session_state.api_url}/docs)"
)
# Main content
uploaded_file = st.file_uploader(
"Choose an image...",
type=['jpg', 'jpeg', 'png', 'webp'],
help="Upload an image of a pet to classify"
)
if uploaded_file is not None:
# Display uploaded image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_container_width=True)
# Classification button
if st.button("🔍 Classify Pet", type="primary"):
with st.spinner("Classifying pet... Please wait."):
try:
# Prepare file for API
files = {'file': (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)}
data = {'top_k': 5}
# Make API request
response = requests.post(
f"{st.session_state.api_url}/predict",
files=files,
data=data,
timeout=30
)
if response.status_code == 200:
result = response.json()
st.success("Classification complete!")
# Display predictions
st.subheader("Top Predictions:")
predictions = result.get('predictions', [])
if predictions:
# Create columns for better layout
cols = st.columns(min(len(predictions), 3))
for idx, pred in enumerate(predictions):
col_idx = idx % 3
with cols[col_idx]:
confidence = pred['confidence'] * 100
class_name = pred['class_name']
# Progress bar for confidence
st.metric(
label=class_name,
value=f"{confidence:.1f}%"
)
st.progress(confidence / 100)
# Show top prediction prominently
top_pred = predictions[0]
st.markdown("---")
st.markdown(f"### 🏆 Top Prediction: **{top_pred['class_name']}**")
st.markdown(f"**Confidence:** {top_pred['confidence'] * 100:.2f}%")
# Show all predictions in a table
with st.expander("View all predictions"):
import pandas as pd
df = pd.DataFrame(predictions)
df['confidence'] = df['confidence'].apply(lambda x: f"{x*100:.2f}%")
df = df.rename(columns={
'class_name': 'Pet Breed',
'confidence': 'Confidence',
'class_id': 'Class ID'
})
st.dataframe(df[['Pet Breed', 'Confidence']], use_container_width=True)
else:
st.warning("No predictions returned.")
else:
st.error(f"API Error: {response.status_code}")
st.json(response.json() if response.content else {"error": "No response"})
except requests.exceptions.RequestException as e:
st.error(f"Failed to connect to API: {str(e)}")
st.info(f"Make sure the API is running at {st.session_state.api_url}")
except Exception as e:
st.error(f"An error occurred: {str(e)}")
# Footer
st.markdown("---")
st.markdown(
"""
<div style='text-align: center; color: #666;'>
<p>Powered by CV200 Pet Classifier API</p>
<p><a href="{}/docs" target="_blank">View API Documentation</a></p>
</div>
""".format(st.session_state.api_url),
unsafe_allow_html=True
)
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