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from fastapi import FastAPI,UploadFile,File |
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from pydantic import BaseModel |
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import pickle |
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import json |
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import pandas as pd |
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from tensorflow.keras.models import load_model |
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from tensorflow.keras.preprocessing import image |
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from tensorflow.keras.applications.inception_v3 import preprocess_input |
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import numpy as np |
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import os |
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import gdown |
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import lightgbm as lgb |
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from PIL import Image |
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CHUNK_SIZE = 1024 |
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app = FastAPI( |
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title='Farmer Buddy API', |
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description='API for Farmer Buddy App', |
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) |
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class crop_recommend_input(BaseModel): |
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N : int |
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P : int |
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K : int |
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temperature : float |
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humidity : float |
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ph : float |
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rainfall : float |
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class crop_yield_input(BaseModel): |
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State_Name : str |
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District_Name : str |
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Season : str |
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Crop : str |
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Area : float |
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Production : float |
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id = "1AWo5bjBSjtVRZlTcdvF1MHAXfvAgFrny" |
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output = "modelcrops.zip" |
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gdown.download(id=id, output=output, quiet=False) |
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from zipfile import ZipFile |
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with ZipFile("modelcrops.zip", 'r') as zObject: |
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zObject.extractall( |
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path="") |
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os.remove(str("modelcrops.zip")) |
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crop_recommend_ml = pickle.load(open('CropRecommendationSystem','rb')) |
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crop_yield_ml = pickle.load(open('CropYieldPrediction.pkl','rb')) |
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crop_disease_ml=load_model('CropDiseaseDetection.h5') |
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@app.post('/croprecommend') |
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def croprecommend(input_parameters : crop_recommend_input): |
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input_data = input_parameters.json() |
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input_dictionary = json.loads(input_data) |
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N = input_dictionary['N'] |
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P = input_dictionary['P'] |
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K = input_dictionary['K'] |
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temperature = input_dictionary['temperature'] |
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humidity = input_dictionary['humidity'] |
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ph = input_dictionary['ph'] |
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rainfall = input_dictionary['rainfall'] |
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input_list = [N, P, K, temperature, humidity, ph, rainfall] |
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prediction = crop_recommend_ml.predict([input_list]) |
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print(prediction[0]) |
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return {"crop":str(prediction[0])} |
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@app.post('/cropyield') |
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def cropyield(input_parameters : crop_yield_input): |
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input_data = input_parameters.json() |
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input_dictionary = json.loads(input_data) |
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State_Name = input_dictionary['State_Name'] |
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District_Name = input_dictionary['District_Name'] |
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Season = input_dictionary['Season'] |
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Crop = input_dictionary['Crop'] |
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Area = input_dictionary['Area'] |
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Production = input_dictionary['Production'] |
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input_list = [State_Name, District_Name, Season, Crop, Area, Production] |
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df = pd.DataFrame([input_list], columns=['State_Name', 'District_Name', 'Season', 'Crop', 'Area' ,'Production']) |
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prediction = crop_yield_ml.predict(df) |
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return {"yield":float(prediction[0])} |
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@app.post('/cropdisease') |
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async def cropdisease(file: UploadFile = File(...)): |
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try: |
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contents = file.file.read() |
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with open(file.filename, 'wb') as f: |
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f.write(contents) |
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except Exception: |
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return {"message": "There was an error uploading the file"} |
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finally: |
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file.file.close() |
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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'] |
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img=image.load_img(str(file.filename),target_size=(224,224)) |
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x=image.img_to_array(img) |
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x=x/255 |
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x=np.expand_dims(x,axis=0) |
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img_data=preprocess_input(x) |
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prediction = crop_disease_ml.predict(img_data) |
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predictions = list(prediction[0]) |
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max_num = max(predictions) |
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index = predictions.index(max_num) |
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print(classes[index]) |
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os.remove(str(file.filename)) |
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return {"disease":classes[index]} |
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