1st commit

app.py

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+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.")

requirements.txt

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+streamlit