Rename api.py to app.py

api.py

@@ -1,55 +0,0 @@
-from fastapi import FastAPI,UploadFile,File
-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
-
-CHUNK_SIZE = 1024
-
-app = FastAPI(
-    title='Flower Classification API',
-    description='API for Flower Classification',
-)
-
-id = "1ry4L9L1-kyc79F1MnYMemJ5P81Gr_mHP"
-output = "model_flowers_classification.h5"
-gdown.download(id=id, output=output, quiet=False)
-# from zipfile import ZipFile
-# with ZipFile("modelcrops.zip", 'r') as zObject:
-#     zObject.extractall(
-#         path="")
-    
-
-predict_ml=load_model('model_flowers_classification.h5')
-
-
-@app.post('/predict')
-async def flowerpredict(file: UploadFile = File(...)):
-    try:
-        contents = file.file.read()
-        with open(file.filename, 'wb') as f:
-            f.write(contents)
-    except Exception:
-        return {"message": "There was an error uploading the file"}
-    finally:
-        file.file.close()
-    classes = ['Lilly','Lotus','Orchid','Sunflower', 'Tulip']
-    img=image.load_img(str(file.filename),target_size=(224,224))
-    x=image.img_to_array(img)
-    x=x/255
-    img_data=np.expand_dims(x,axis=0)
-    prediction = predict_ml.predict(img_data)
-    predictions = list(prediction[0])
-    max_num = max(predictions)
-    index = predictions.index(max_num)
-    print(classes[index])
-    os.remove(str(file.filename))
-    return {"output":classes[index]}

app.py

@@ -0,0 +1,54 @@
+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
+
+
+app = Flask(__name__)
+
+id = "1ry4L9L1-kyc79F1MnYMemJ5P81Gr_mHP"
+output = "model_flowers_classification.h5"
+gdown.download(id=id, output=output, quiet=False)
+   
+
+crop_disease_ml=load_model('model_flowers_classification.h5')
+
+@app.route("/upload-image", methods=["POST"])
+def upload_image():
+    # if request.method == "POST":
+        if request.files:
+            imag = request.files["image"]
+            try:
+                contents = imag.file.read()
+                with open(imag.filename, 'wb') as f:
+                    f.write(contents)
+            except Exception:
+                return {"message": "There was an error uploading the file"}
+            finally:
+                imag.file.close()
+            print(imag)
+            classes = ['Lilly','Lotus','Orchid','Sunflower', 'Tulip']
+            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))
+            return {"output":classes[index]}
+
+
+if __name__ =="__main__":
+    app.run()