updated app.py

flask_orgiC/app.py

@@ -18,14 +18,17 @@ def allowed_file(filename):
            filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
 
 
-def predict(file):
+def predict_disease(file):
     img  = load_img(file, target_size=IMAGE_SIZE)
     img = img_to_array(img)/255.0
     img = np.expand_dims(img, axis=0)
     probs = img2.predict(img)[0]
     output = {'Healthy' : probs[0],'Sigatoka': probs[1]} 
+    healthy = probs[0]*100
+    sigatoka = probs[1]*100
+    output = {probs[0],probs[1]} 
     
-    return output
+    return probs
 
 
 
@@ -39,19 +42,20 @@ def template_test():
 
 
 @app.route('/', methods=['GET', 'POST'])
-def upload_file():
+def upload_desease_file():
     if request.method == 'POST':
         file = request.files['file']
-        
         if file and allowed_file(file.filename):
             filename = secure_filename(file.filename)
-            file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-            file.save(file_path)
-            output = predict(file_path)
+            file_path1 = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+            file.save(file_path1)
+            output = predict_disease(file_path1)
+           # output = {probs[0],probs[1]} 
+            healthy = output[0]*100
+            sigatoka = output[1]*100
             
            
-           
-    return render_template("disease.html", label=output, imagesource=file_path)
+    return render_template("disease.html", label1=healthy,label2=sigatoka, imagesource=file_path1)
 
 
 @app.route('/uploads/<filename>')

flask_orgiC/disease_recognition.py

+# -*- coding: utf-8 -*-
+"""disease recognition.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1KZ0kUs19r-B0Rs7f0N5oz5nzH4y9NkUd
+"""
+
+import cv2
+import os
+data_path='/content/drive/MyDrive/CDAP/Banana Leaf Disease'
+categories=os.listdir(data_path)
+labels=[i for i in range(len(categories))]
+
+label_dict=dict(zip(categories,labels))
+print(label_dict)
+print(categories)
+print(labels)
+
+img_size=224
+data=[]
+target=[]
+
+for category in categories:
+    folder_path=os.path.join(data_path,category)
+    img_names=os.listdir(folder_path)
+        
+    for img_name in img_names:
+        img_path=os.path.join(folder_path,img_name)
+        img=cv2.imread(img_path)
+
+        try:  
+            resized=cv2.resize(img,(img_size,img_size))
+            data.append(resized)
+            target.append(label_dict[category])
+          
+        except Exception as e:
+            print('Exception:',e)
+
+import numpy as np
+
+data=np.array(data)/255.0
+data=np.reshape(data,(data.shape[0],img_size,img_size,3))
+target=np.array(target)
+
+from keras.utils import np_utils
+
+new_target=np_utils.to_categorical(target)
+
+from keras.models import Sequential
+from keras.layers import Dense,Activation,Flatten,Dropout
+from keras.layers import Conv2D,MaxPooling2D
+from keras.callbacks import ModelCheckpoint
+
+model=Sequential()
+
+model.add(Conv2D(200,(3,3),input_shape=data.shape[1:]))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2,2)))
+
+
+model.add(Conv2D(100,(3,3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2,2)))
+
+
+model.add(Flatten())
+model.add(Dropout(0.5))
+model.add(Dense(50,activation='relu'))
+model.add(Dense(2,activation='softmax'))
+
+
+model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
+
+model.summary()
+
+from sklearn.model_selection import train_test_split
+train_data,test_data,train_target,test_target=train_test_split(data,new_target,test_size=0.2)
+
+train_data.shape
+
+train_target.shape
+
+test_target.shape
+
+history=model.fit(train_data,train_target,epochs=75,validation_split=0.2)
+
+model.save('/content/drive/MyDrive/CDAP/disease_modelrp.h5')
+
+from matplotlib import pyplot as plt
+
+N = 75
+plt.style.use("ggplot")
+plt.figure()
+plt.plot(np.arange(0, N), history.history["loss"], label="train_loss")
+plt.plot(np.arange(0, N), history.history["val_loss"], label="val_loss")
+plt.plot(np.arange(0, N), history.history["accuracy"], label="train_acc")
+plt.plot(np.arange(0, N), history.history["val_accuracy"], label="val_acc")
+plt.title("Training Loss and Accuracy")
+plt.xlabel("Epoch #")
+plt.ylabel("Loss/Accuracy")
+plt.legend(loc="center right")
+plt.savefig("CNN_Model")
\ No newline at end of file

flask_orgiC/templates/disease.html

@@ -36,33 +36,55 @@
     </form>
     -->
     </div>
-    <p style="margin-bottom:2cm;"></p>
+     <p style="margin-bottom:2cm;"></p>
+
+    <div class="row">
+      <div class="col-8">
+        <div class="page-header">
+          <h3 id="tables">Result</h3>
+        </div>
+      </div>
+      <div class="col-4">col-4</div>
+    </div>
     <div class="row">
         <div class="col-lg-4">
           <div class="page-header">
             <h3 id="tables">Result</h3>
           </div>
           <div class="bs-component">
+            <img width="400" height="400" src="{{imagesource}}" /> 
             <table class="table table-hover">
                 <tr class="table-active">
                   <!-- <th scope="col">Image</th> -->
                   <th scope="col">Predict</th>
                 </tr>
                 <tr>
-                    {% if label %}
-                    <th scope="row"> <img width="224" height="224" src="{{imagesource}}" /> </th>
-                   
+                 
+                    <th scope="row">Leaf Type</th>
+                    <th scope="row">Accuracy</th>
+
+                 
                              
                   </tr>
                   <tr>
                                                  
-                    <td> Prediction : <i> {{label }}</i></td>
+                    <td> Healthy : </td>
+                    <td> Sigatoka : </td>
                     
-                    {% endif %}
+                  
                   </tr>
+                  <tr>
+                    <td><i> {{label1 }}</i></td>
+                    <td><i> {{label2 }}</i></td>
+
+                  </tr>
+
+
             </table> 
         </div>
       </div>
+        </div>
+      </div>
 
       <p>     </p>
       <p>    </p>