Create app.py

app.py

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+from flask import Flask, request, jsonify ,render_template , redirect
+from pydantic import BaseModel
+import pickle
+import json
+import pandas as pd
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.inception_v3 import preprocess_input
+import numpy as np
+import os
+import gdown
+import lightgbm as lgb
+from PIL import Image
+from flask_cors import CORS, cross_origin
+
+
+app = Flask(__name__)
+
+id = "1dPrnyH7y9ojSHaOOOTkbGkCnhwYvMxab"
+output = "disease_new.h5"
+gdown.download(id=id, output=output, quiet=False)
+   
+CORS(app)
+app.config['CORS_HEADERS'] = 'Content-Type'
+
+crop_disease_ml=load_model('disease_new.h5')
+
+@app.route("/upload-image", methods=["POST"])
+@cross_origin()
+def upload_image():
+    # if request.method == "POST":
+        if request.files:
+            imag = request.files["image"]
+            try:
+                contents = imag.read()
+                with open(imag.filename, 'wb') as f:
+                    f.write(contents)
+            except Exception:
+                return {"message": "There was an error uploading the file"}
+            finally:
+                imag.close()
+            print(imag)
+            classes = ['Pepper bell  Bacterial spot', 'Pepper bell  healthy', 'Potato  Early blight', 'Potato  Late blight', 'Potato  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 YellowLeaf Curl Virus', 'Tomato Tomato mosaic virus', 'Tomato healthy']
+            img=image.load_img(str(imag.filename),target_size=(224,224))
+            x=image.img_to_array(img)
+            x=x/255
+            img_data=np.expand_dims(x,axis=0)
+            prediction = crop_disease_ml.predict(img_data)
+            predictions = list(prediction[0])
+            max_num = max(predictions)
+            index = predictionsfrom flask import Flask, request, jsonify ,render_template , redirect
+from pydantic import BaseModel
+import pickle
+import json
+import pandas as pd
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.inception_v3 import preprocess_input
+import numpy as np
+import os
+import gdown
+import lightgbm as lgb
+from PIL import Image
+from flask_cors import CORS, cross_origin
+
+
+app = Flask(__name__)
+
+id = "1dPrnyH7y9ojSHaOOOTkbGkCnhwYvMxab"
+output = "disease_new.h5"
+gdown.download(id=id, output=output, quiet=False)
+   
+CORS(app)
+app.config['CORS_HEADERS'] = 'Content-Type'
+
+crop_disease_ml=load_model('disease_new.h5')
+
+@app.route("/upload-image", methods=["POST"])
+@cross_origin()
+def upload_image():
+    # if request.method == "POST":
+        if request.files:
+            imag = request.files["image"]
+            try:
+                contents = imag.read()
+                with open(imag.filename, 'wb') as f:
+                    f.write(contents)
+            except Exception:
+                return {"message": "There was an error uploading the file"}
+            finally:
+                imag.close()
+            print(imag)
+            classes = ['Pepper__bell___Bacterial_spot', 'Pepper__bell___healthy', 'Potato___Early_blight', 'Potato___Late_blight', 'Potato___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_YellowLeaf__Curl_Virus', 'Tomato__Tomato_mosaic_virus', 'Tomato_healthy']
+            img=image.load_img(str(imag.filename),target_size=(224,224))
+            x=image.img_to_array(img)
+            x=x/255
+            img_data=np.expand_dims(x,axis=0)
+            prediction = crop_disease_ml.predict(img_data)
+            predictions = list(prediction[0])
+            max_num = max(predictions)
+            index = predictions.index(max_num)
+            print(classes[index])
+            os.remove(str(imag.filename))
+            response = jsonify(output=classes[index])
+            # response.headers.add('Access-Control-Allow-Origin', '*')
+            # response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
+            # response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
+            return response
+
+
+if __name__ =="__main__":
+    app.run(debug=False,host="0.0.0.0",port=5000).index(max_num)
+            print(classes[index])
+            os.remove(str(imag.filename))
+            response = jsonify(output=classes[index])
+            # response.headers.add('Access-Control-Allow-Origin', '*')
+            # response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
+            # response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
+            return response
+
+
+if __name__ =="__main__":
+    app.run(debug=False,host="0.0.0.0",port=5000)