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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]}
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