Create handler.py

handler.py

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+import streamlit as st
+import numpy as np
+import pickle
+
+# Load your model and scalers
+model = pickle.load(open('model.pkl', 'rb'))
+sc = pickle.load(open('standscaler.pkl', 'rb'))
+ms = pickle.load(open('minmaxscaler.pkl', 'rb'))
+
+# Title of the web app
+st.title("Crop Recommendation System 🌱")
+
+# Create a sidebar for input fields
+with st.sidebar:
+    st.header("Input Parameters")
+    N = st.number_input("Nitrogen content", min_value=0.0, value=0.0, step=0.1, format="%.1f", help="Enter Nitrogen content")
+    P = st.number_input("Phosphorus content", min_value=0.0, value=0.0, step=0.1, format="%.1f", help="Enter Phosphorus content")
+    K = st.number_input("Potassium content", min_value=0.0, value=0.0, step=0.1, format="%.1f", help="Enter Potassium content")
+    temp = st.number_input("Temperature in °C", min_value=0.0, value=0.0, step=0.1, format="%.1f", help="Enter Temperature in °C")
+    humidity = st.number_input("Humidity in %", min_value=0.0, value=0.0, step=0.1, format="%.1f", help="Enter Humidity in %")
+    ph = st.number_input("pH value", min_value=0.0, value=0.0, step=0.1, format="%.1f", help="Enter pH value")
+    rainfall = st.number_input("Rainfall in mm", min_value=0.0, value=0.0, step=0.1, format="%.1f", help="Enter Rainfall in mm")
+
+if st.button("Get Recommendation"):
+    feature_list = [N, P, K, temp, humidity, ph, rainfall]
+    single_pred = np.array(feature_list).reshape(1, -1)
+
+    scaled_features = ms.transform(single_pred)
+    final_features = sc.transform(scaled_features)
+    prediction = model.predict(final_features)
+
+    crop_dict = {1: "Rice", 2: "Maize", 3: "Jute", 4: "Cotton", 5: "Coconut", 6: "Papaya", 7: "Orange",
+                 8: "Apple", 9: "Muskmelon", 10: "Watermelon", 11: "Grapes", 12: "Mango", 13: "Banana",
+                 14: "Pomegranate", 15: "Lentil", 16: "Blackgram", 17: "Mungbean", 18: "Mothbeans",
+                 19: "Pigeonpeas", 20: "Kidneybeans", 21: "Chickpea", 22: "Coffee"}
+
+    if prediction[0] in crop_dict:
+        crop = crop_dict[prediction[0]]
+        result = f"{crop} is the best crop to be cultivated right there."
+    else:
+        result = "Sorry, we could not determine the best crop to be cultivated with the provided data."
+
+    st.success(result)
+
+# Footer
+st.markdown("""
+    <style>
+        .footer {
+            position: fixed;
+            left: 0;
+            bottom: 0;
+            width: 100%;
+            background-color: #f1f1f1;
+            color: #555;
+            text-align: center;
+            padding: 10px;
+        }
+    </style>
+    <div class="footer">
+        Powered by Streamlit
+    </div>
+    """, unsafe_allow_html=True)