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Procfile

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+web: gunicorn -w 4 -b 0.0.0.0:$PORT application:application

README.md

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+# Crop Recommendation API
+
+This project implements a crop recommendation system using a Random Forest classifier with an accuracy of 99.18%. It is deployed and accessible at [cropok-production.up.railway.api](https://cropok-production.up.railway.app). 
+
+Now, URL is not working because free trial limit is exceeded.
+
+## Features
+
+- Utilizes Random Forest algorithm for accurate crop recommendations.
+- Converts the model into a Flask API for easy integration.
+
+## Input Parameters
+- Nitrogen
+- Phosphorus
+- Potassium
+- Temperature
+- Humidity
+- Ph
+
+## Note
+
+The API is currently not working due to the free trial limit being exceeded. You are free to deploy and use it in your own environment.

application.py

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+from flask import Flask, request, jsonify
+import numpy as np
+import sklearn
+import pickle
+
+model = pickle.load(open('model.pkl', 'rb'))
+sc = pickle.load(open('standscaler.pkl', 'rb'))
+ms = pickle.load(open('minmaxscaler.pkl', 'rb'))
+application = Flask(__name__)
+
+@application.route('/')
+def ok():
+    return "Running!"
+    
+@application.route('/pred', methods=['POST'])
+def predict():
+    N = request.form['Nitrogen']
+    P = request.form['Phosphorus']
+    K = request.form['Potassium']
+    temp = request.form['Temperature']
+    humidity = request.form['Humidity']
+    ph = request.form['Ph']
+    rainfall = request.form['Rainfall']
+
+    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]]
+    else:
+        crop = 'NOT able to recommend'
+    return jsonify(crop)
+
+
+# python main
+if __name__ == "__main__":
+    application.run(host='0.0.0.0', port=5000, debug=True)

minmaxscaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:509dcc63a697ff54794ab75b34cf7b021437682b192b93b2e19e28f61c60ab16
+size 760

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6fb0624dbcc05f5e1fd35f1a5ed6f0e2e15756fe2794584dc32b86ad124eba5
+size 3502048

requirements.txt

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+annotated-types
+anyio
+blinker
+click
+colorama
+fastapi
+Flask
+gunicorn
+h11
+idna
+itsdangerous
+Jinja2
+joblib==1.3.0
+MarkupSafe
+nltk
+numpy
+pydantic
+pydantic_core
+regex
+scikit-learn==1.3.0
+scipy
+sniffio
+starlette
+textblob
+threadpoolctl
+tqdm
+typing_extensions
+uvicorn
+Werkzeug

runtime.txt

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+python-3.11.*

standscaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:639511bb6e216cf1f1748fa54f40483be288875502bdf19ac9b98891f2d242b1
+size 617