Upload 2 files

api.py

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+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='Farmer Buddy API',
+    description='API for Farmer Buddy App',
+)
+
+class crop_recommend_input(BaseModel):
+    N : int
+    P : int
+    K : int
+    temperature : float
+    humidity : float
+    ph : float
+    rainfall : float
+
+class crop_yield_input(BaseModel):
+    State_Name : str
+    District_Name : str
+    Season : str
+    Crop : str
+    Area : float
+    Production : float
+id = "1AWo5bjBSjtVRZlTcdvF1MHAXfvAgFrny"
+output = "modelcrops.zip"
+gdown.download(id=id, output=output, quiet=False)
+from zipfile import ZipFile
+with ZipFile("modelcrops.zip", 'r') as zObject:
+    zObject.extractall(
+        path="")
+    
+os.remove(str("modelcrops.zip"))
+crop_recommend_ml = pickle.load(open('CropRecommendationSystem','rb'))
+crop_yield_ml = pickle.load(open('CropYieldPrediction.pkl','rb'))
+crop_disease_ml=load_model('CropDiseaseDetection.h5')
+
+@app.post('/croprecommend')
+def croprecommend(input_parameters : crop_recommend_input):
+
+    input_data = input_parameters.json()
+    input_dictionary = json.loads(input_data)
+    N = input_dictionary['N']
+    P = input_dictionary['P']
+    K = input_dictionary['K']
+    temperature = input_dictionary['temperature']
+    humidity = input_dictionary['humidity']
+    ph = input_dictionary['ph']
+    rainfall = input_dictionary['rainfall']
+    input_list = [N, P, K, temperature, humidity, ph, rainfall]
+    prediction = crop_recommend_ml.predict([input_list])
+    print(prediction[0])
+    return {"crop":str(prediction[0])}
+
+@app.post('/cropyield')
+def cropyield(input_parameters : crop_yield_input):
+
+    input_data = input_parameters.json()
+    input_dictionary = json.loads(input_data)
+    State_Name = input_dictionary['State_Name']
+    District_Name = input_dictionary['District_Name']
+    Season = input_dictionary['Season']
+    Crop = input_dictionary['Crop']
+    Area = input_dictionary['Area']
+    Production = input_dictionary['Production']
+    input_list = [State_Name, District_Name, Season, Crop, Area, Production]
+    # df = pd.DataFrame([['Chhattisgarh',	'BEMETARA',	'Rabi'	,'Potato',	3.0	,20.0]], columns=['State_Name',	'District_Name',	'Season',	'Crop',	'Area'	,'Production'])
+    df = pd.DataFrame([input_list], columns=['State_Name',	'District_Name',	'Season',	'Crop',	'Area'	,'Production'])
+    prediction = crop_yield_ml.predict(df)
+    return {"yield":float(prediction[0])}
+
+@app.post('/cropdisease')
+async def cropdisease(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 = ['Potato___Early_blight', 'Tomato_healthy', 'Tomato__Target_Spot', 'Tomato__Tomato_mosaic_virus', 'Tomato_Septoria_leaf_spot', 'Tomato_Bacterial_spot', 'Tomato_Spider_mites_Two_spotted_spider_mite', 'Tomato_Early_blight', 'Tomato_Late_blight', 'Pepper__bell___healthy', 'Tomato__Tomato_YellowLeaf__Curl_Virus', 'Potato___healthy', 'Tomato_Leaf_Mold', 'Potato___Late_blight', 'Pepper__bell___Bacterial_spot']
+    img=image.load_img(str(file.filename),target_size=(224,224))
+    x=image.img_to_array(img)
+    x=x/255
+    x=np.expand_dims(x,axis=0)
+    img_data=preprocess_input(x)
+    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(file.filename))
+    return {"disease":classes[index]}

requirements.txt

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+fastapi
+pydantic
+pandas
+tensorflow
+numpy
+uvicorn
+lightgbm
+gdown
+python-multipart
+Pillow