Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.linear_model import LinearRegression | |
| import joblib | |
| from huggingface_hub import hf_hub_download, upload_file | |
| # Load or initialize the model | |
| def load_model(): | |
| try: | |
| # Attempt to download model from Hugging Face | |
| model_path = hf_hub_download(repo_id="your-huggingface-repo", filename="crop_yield_model.pkl") | |
| model = joblib.load(model_path) | |
| return model | |
| except Exception as e: | |
| st.warning("Model not found on Hugging Face. Initializing a new one.") | |
| # Initialize with dummy training data | |
| model = LinearRegression() | |
| dummy_features = np.array([[100, 50, 25]]) # Example realistic values for rainfall, fertilizer, temperature | |
| dummy_target = np.array([2]) # Example realistic yield value | |
| model.fit(dummy_features, dummy_target) | |
| return model | |
| def save_model(model): | |
| joblib.dump(model, "crop_yield_model.pkl") | |
| upload_file( | |
| path_or_fileobj="crop_yield_model.pkl", | |
| path_in_repo="crop_yield_model.pkl", | |
| repo_id="your-huggingface-repo", | |
| repo_type="model", | |
| ) | |
| # Streamlit app | |
| st.title("Crop Yield Prediction") | |
| # Input features | |
| st.sidebar.header("Input Features") | |
| rainfall = st.sidebar.number_input("Rainfall (mm)", min_value=0.0, max_value=5000.0, step=0.1) | |
| fertilizer = st.sidebar.number_input("Fertilizer Used (kg/ha)", min_value=0.0, max_value=1000.0, step=0.1) | |
| temperature = st.sidebar.number_input("Temperature (°C)", min_value=-10.0, max_value=50.0, step=0.1) | |
| # Train new model option | |
| train_new = st.sidebar.checkbox("Train New Model") | |
| # Load data for training if needed | |
| if train_new: | |
| st.sidebar.header("Training Data") | |
| uploaded_file = st.sidebar.file_uploader("Upload a CSV file", type="csv") | |
| if uploaded_file: | |
| data = pd.read_csv(uploaded_file) | |
| st.write("### Training Data Preview", data.head()) | |
| features = data[["Rainfall", "Fertilizer", "Temperature"]] | |
| target = data["Yield"] | |
| # Train model | |
| model = LinearRegression() | |
| model.fit(features, target) | |
| save_model(model) | |
| st.success("Model trained and saved successfully!") | |
| else: | |
| st.warning("Please upload a CSV file to train a new model.") | |
| else: | |
| model = load_model() | |
| # Predict crop yield | |
| if st.button("Predict Crop Yield"): | |
| if model is not None: | |
| input_data = np.array([[rainfall, fertilizer, temperature]]) | |
| try: | |
| prediction = model.predict(input_data) | |
| st.write(f"### Predicted Crop Yield: {prediction[0]:.2f} tons/ha") | |
| except Exception as e: | |
| st.error("An error occurred during prediction. Please ensure the model is properly trained.") | |
| else: | |
| st.warning("Model not found or not trained. Please train a new model.") | |
| # Notes for Hugging Face integration | |
| st.sidebar.markdown("---") | |
| st.sidebar.write("Hugging Face Integration:") | |
| st.sidebar.write("- Replace `your-huggingface-repo` with your repository name.") | |
| st.sidebar.write("- Ensure the repository permissions allow model uploads and downloads.") | |