Update handler.py

handler.py

@@ -1,62 +1,34 @@
-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)
+import numpy as np
+from flask import Flask, request, jsonify
+
+# Load the pickle model
+MODEL_PATH = "/mnt/data/model.pkl"
+
+app = Flask(__name__)
+
+# Load the model
+with open(MODEL_PATH, 'rb') as file:
+    model = pickle.load(file)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Parse input JSON data
+        input_data = request.get_json()
+        if not input_data:
+            return jsonify({"error": "Invalid input data"}), 400
+        
+        # Assuming the input data is a list of features
+        features = np.array(input_data['features']).reshape(1, -1)  # Adjust for single input
+
+        # Make predictions
+        prediction = model.predict(features)
+        
+        # Return the prediction as JSON
+        return jsonify({"prediction": prediction.tolist()}), 200
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)