Commit1

Data/fertilizer.csv

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+,Crop,N,P,K,pH,soil_moisture
+0,rice,80,40,40,5.5,30
+3,maize,80,40,20,5.5,50
+5,chickpea,40,60,80,5.5,60
+12,kidneybeans,20,60,20,5.5,45
+13,pigeonpeas,20,60,20,5.5,45
+14,mothbeans,20,40,20,5.5,30
+15,mungbean,20,40,20,5.5,80
+18,blackgram,40,60,20,5,60
+24,lentil,20,60,20,5.5,90
+60,pomegranate,20,10,40,5.5,30
+61,banana,100,75,50,6.5,40
+62,mango,20,20,30,5,15
+63,grapes,20,125,200,4,60
+66,watermelon,100,10,50,5.5,70
+67,muskmelon,100,10,50,5.5,30
+69,apple,20,125,200,6.5,50
+74,orange,20,10,10,4,60
+75,papaya,50,50,50,6,20
+88,coconut,20,10,30,5,45
+93,cotton,120,40,20,5.5,70
+94,jute,80,40,40,5.5,20
+95,coffee,100,20,30,5.5,20

Procfile

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+web: gunicorn app:app --log-level debug

Runtime.txt

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+python-3.6.12

app.py

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+# Importing essential libraries and modules
+
+
+from flask import Flask, render_template, request, Markup,redirect
+import numpy as np
+import pandas as pd
+from utils.disease import disease_dic
+import requests
+import pickle
+import io
+import torch
+from torchvision import transforms
+from PIL import Image
+from utils.model import ResNet9
+# ==============================================================================================
+
+# -------------------------LOADING THE TRAINED MODELS -----------------------------------------------
+
+# Loading plant disease classification model
+
+disease_classes = ['Apple___Apple_scab',
+                   'Apple___Black_rot',
+                   'Apple___Cedar_apple_rust',
+                   'Apple___healthy',
+                   'Blueberry___healthy',
+                   'Cherry_(including_sour)___Powdery_mildew',
+                   'Cherry_(including_sour)___healthy',
+                   'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',
+                   'Corn_(maize)___Common_rust_',
+                   'Corn_(maize)___Northern_Leaf_Blight',
+                   'Corn_(maize)___healthy',
+                   'Grape___Black_rot',
+                   'Grape___Esca_(Black_Measles)',
+                   'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
+                   'Grape___healthy',
+                   'Orange___Haunglongbing_(Citrus_greening)',
+                   'Peach___Bacterial_spot',
+                   'Peach___healthy',
+                   'Pepper,_bell___Bacterial_spot',
+                   'Pepper,_bell___healthy',
+                   'Potato___Early_blight',
+                   'Potato___Late_blight',
+                   'Potato___healthy',
+                   'Raspberry___healthy',
+                   'Soybean___healthy',
+                   'Squash___Powdery_mildew',
+                   'Strawberry___Leaf_scorch',
+                   'Strawberry___healthy',
+                   'Tomato___Bacterial_spot',
+                   'Tomato___Early_blight',
+                   'Tomato___Late_blight',
+                   'Tomato___Leaf_Mold',
+                   'Tomato___Septoria_leaf_spot',
+                   'Tomato___Spider_mites Two-spotted_spider_mite',
+                   'Tomato___Target_Spot',
+                   'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
+                   'Tomato___Tomato_mosaic_virus',
+                   'Tomato___healthy']
+
+disease_model_path = 'models/plant_disease_model.pth'
+disease_model = ResNet9(3, len(disease_classes))
+disease_model.load_state_dict(torch.load(
+    disease_model_path, map_location=torch.device('cpu')))
+disease_model.eval()
+
+
+# Loading crop recommendation model
+
+crop_recommendation_model_path = 'models/RandomForest.pkl'
+crop_recommendation_model = pickle.load(
+    open(crop_recommendation_model_path, 'rb'))
+
+
+# =========================================================================================
+
+# Custom functions for calculations
+
+def predict_image(img, model=disease_model):
+    """
+    Transforms image to tensor and predicts disease label
+    :params: image
+    :return: prediction (string)
+    """
+    transform = transforms.Compose([
+        transforms.Resize(256),
+        transforms.ToTensor(),
+    ])
+    image = Image.open(io.BytesIO(img))
+    img_t = transform(image)
+    img_u = torch.unsqueeze(img_t, 0)
+
+    # Get predictions from model
+    yb = model(img_u)
+    # Pick index with highest probability
+    _, preds = torch.max(yb, dim=1)
+    prediction = disease_classes[preds[0].item()]
+    # Retrieve the class label
+    return prediction
+
+# ===============================================================================================
+# ------------------------------------ FLASK APP -------------------------------------------------
+
+
+app = Flask(__name__)
+
+# render home page
+
+
+@ app.route('/')
+def home():
+    title = 'Farmers Friend - Home'
+    return render_template('index.html', title=title)
+
+# render crop recommendation form page
+
+
+@ app.route('/crop-recommend')
+def crop_recommend():
+    title = 'Farmers Friend - Crop Recommendation'
+    return render_template('crop.html', title=title)
+
+
+# ===============================================================================================
+
+# RENDER PREDICTION PAGES
+
+# render crop recommendation result page
+
+
+@ app.route('/crop-predict', methods=['POST'])
+def crop_prediction():
+    title = 'Farmers Friend- Crop Recommendation'
+
+    if request.method == 'POST':
+        N = int(request.form['nitrogen'])
+        P = int(request.form['phosphorous'])
+        K = int(request.form['pottasium'])
+        temperature = float(request.form['temperature'])
+        humidity = float(request.form['humidity'])
+        ph = float(request.form['ph'])
+        rainfall = float(request.form['rainfall'])
+
+        data = np.array([[N, P, K, temperature, humidity, ph, rainfall]])
+        my_prediction = crop_recommendation_model.predict(data)
+        final_prediction = my_prediction[0]
+
+        return render_template('crop-result.html', prediction=final_prediction, title=title)
+
+
+# render disease prediction result page
+
+
+@app.route('/disease-predict', methods=['GET', 'POST'])
+def disease_prediction():
+    title = 'Farmers Friend - Disease Detection'
+
+    if request.method == 'POST':
+        if 'file' not in request.files:
+            return redirect(request.url)
+        file = request.files.get('file')
+        if not file:
+            return render_template('disease.html', title=title)
+        try:
+            img = file.read()
+
+            prediction = predict_image(img)
+
+            prediction = Markup(str(disease_dic[prediction]))
+            return render_template('disease-result.html', prediction=prediction, title=title)
+        except:
+            pass
+    return render_template('disease.html', title=title)
+
+
+# ===============================================================================================
+if __name__ == '__main__':
+    app.run(debug=False)