Upload app.py

app.py

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+#Import necessary libraries
+from flask import Flask, render_template, request
+
+import numpy as np
+import os
+
+from keras.preprocessing.image import load_img
+from keras.preprocessing.image import img_to_array
+from keras.models import load_model
+
+#load model
+model =load_model("model/v4_1_pred_stra_dis.h5")
+
+print('@@ Model loaded')
+
+
+def pred_cot_dieas(cott_plant):
+  test_image = load_img(cott_plant, target_size = (150, 150)) # load image 
+  print("@@ Got Image for prediction")
+  
+  test_image = img_to_array(test_image)/255 # convert image to np array and normalize
+  test_image = np.expand_dims(test_image, axis = 0) # change dimention 3D to 4D
+  
+  result = model.predict(test_image).round(3) # predict diseased palnt or not
+  print('@@ Raw result = ', result)
+  
+  pred = np.argmax(result) # get the index of max value
+ 
+  if pred == 0:
+    return "Diseased Strawberry Plant", 'angular_leafspot.html' # if index 0 burned leaf
+  elif pred == 1:
+      return 'Diseased Strawberry Plant', 'grey_mold.html' # # if index 1
+  elif pred == 2:
+      return 'Diseased Strawberry Plant', 'leaf_spot.html'  # if index 2  fresh leaf
+  else:
+    return "Diseased Strawberry Plant", 'powdery_mildew_leaf.html' # if index 3
+
+#------------>>pred_cot_dieas<<--end
+
+# Create flask instance
+app = Flask(__name__)
+
+# render index.html page
+@app.route("/", methods=['GET', 'POST'])
+def home():
+        return render_template('index1.html')
+    
+ 
+# get input image from client then predict class and render respective .html page for solution
+@app.route("/predict", methods = ['GET','POST'])
+def predict():
+     if request.method == 'POST':
+        file = request.files['image'] # fet input
+        filename = file.filename        
+        print("@@ Input posted = ", filename)
+        
+        file_path = os.path.join('static/user uploaded', filename)
+        file.save(file_path)
+
+        print("@@ Predicting class......")
+        pred, output_page = pred_cot_dieas(cott_plant=file_path)
+              
+        return render_template(output_page, pred_output = pred, user_image = file_path)
+    
+# For local system & cloud
+if __name__ == "__main__":
+    app.run(threaded=False,) 
+    
+