Add Flask app and model files for crop prediction API

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.9-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 5000
+
+CMD ["python", "app.py"]

app.py

@@ -0,0 +1,46 @@
+from flask import Flask, request, jsonify
+import pickle
+import numpy as np
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)
+
+# Load the trained model and scaler
+with open('crop_recommendation_model.pkl', 'rb') as model_file:
+    model = pickle.load(model_file)
+
+with open('scaler.pkl', 'rb') as scaler_file:
+    scaler = pickle.load(scaler_file)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get input data from the request
+        data = request.get_json()
+        features = [
+            float(data['N']),
+            float(data['P']),
+            float(data['K']),
+            float(data['temperature']),
+            float(data['humidity']),
+            float(data['ph']),
+            float(data['rainfall'])
+        ]
+
+        # Convert features to numpy array and reshape for prediction
+        features = np.array(features).reshape(1, -1)
+
+        # Scale the input features
+        scaled_features = scaler.transform(features)
+
+        # Make prediction
+        prediction = model.predict(scaled_features)
+
+        # Return raw model output
+        return jsonify({'prediction': prediction[0]})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+if __name__ == '__main__':
+    app.run(debug=True)

crop_recommendation_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87c41241399ae3066cd2dcff2c59de98b9da702d407e691171e2e0781b80ca5f
+size 3605778

requirements.txt

@@ -0,0 +1 @@
+echo -e "flask\nflask-cors\nscikit-learn\nnumpy" > requirements.txt

scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aade8e779aa81d1bc9ea25ed49f4392711081c6519198cf9a6d8868cef9ee51e
+size 757