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import streamlit as st
import pandas as pd
import numpy as np
from huggingface_hub import hf_hub_download
import joblib
# Load the model from Hugging Face
@st.cache_resource
def load_model_from_hf(repo_id, filename):
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
model = joblib.load(file_path)
return model
# App settings
st.set_page_config(page_title="Power Prediction App", layout="centered")
# Sidebar inputs
st.sidebar.title("Model Integration Settings")
repo_id = st.sidebar.text_input("Hugging Face Repo ID", "random_forest_power_model")
filename = st.sidebar.text_input("Model Filename", "model.joblib")
# Main app
st.title("Power Prediction using Random Forest Model")
st.write("Enter the input values for current and voltage to predict power.")
# Load the model
try:
model = load_model_from_hf(repo_id, filename)
st.success("Model loaded successfully from Hugging Face!")
except Exception as e:
st.error(f"Error loading model: {e}")
st.stop()
# User input
current = st.number_input("Enter Current (I) in Amperes:", min_value=0.0, value=10.0, step=0.1)
voltage = st.number_input("Enter Voltage (V) in Volts:", min_value=0.0, value=220.0, step=1.0)
# Predict power
if st.button("Predict Power"):
try:
input_data = pd.DataFrame({"Current": [current], "Voltage": [voltage]})
prediction = model.predict(input_data)
st.success(f"Predicted Power (P): {prediction[0]:.2f} W")
except Exception as e:
st.error(f"Error in prediction: {e}")
# Option to upload new models
st.sidebar.header("Upload a New Model")
uploaded_file = st.sidebar.file_uploader("Upload a .joblib model file", type=["joblib"])
if uploaded_file:
with open("uploaded_model.joblib", "wb") as f:
f.write(uploaded_file.getbuffer())
st.sidebar.success("Model uploaded! Use 'uploaded_model.joblib' as the filename.")
# Footer
st.write("---")
st.write("This app uses a Random Forest model hosted on Hugging Face to predict power.")
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