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import streamlit as st
from tensorflow import image
from keras import models
import numpy as np
from PIL import Image
import pandas as pd
st.title("Rice Disease Classifier 🌾")
desc = pd.read_csv("files/description.csv")
model = models.load_model("models/0.3/model.h5")
dis = list(desc.disease.values)
def image_classifier(inp):
try:
inp = image.resize(inp, (256,256))
inp = np.expand_dims(inp,0)
pred= model.predict(inp)
return dis[np.argmax(pred)] , f"Confidence - {round(max(pred[0])*100,2)}%"
except:
return "Healthy", "Confidence - 0%"
def detail(pro):
x = desc[desc["disease"]==pro]
return list(x["hindi"])[0], list(x["desc"])[0], list(x["hndesc"])[0], list(x["pre"])[0], list(x["hnpre"])[0]
cho = st.file_uploader("Upload Image From Gallery", type=['png','jpg','jpeg','webp'])
img = ""
if cho is not None:
img = Image.open(cho)
st.write("or")
if st.button("Open Camera"):
cam = st.camera_input("Take image")
if cam is not None:
img = Image.open(cam)
if st.button("Detect"):
col1,col2,col3 = st.columns(3)
pro, conf = image_classifier(img)
hin, des, hnd, pre, hnp = detail(pro)
try:
with col2:
st.image(img)
st.write("\n\n")
st.header(pro)
st.subheader(f"({hin})")
st.subheader(conf)
st.write("\n\n\n\n")
st.subheader(f"Description :")
st.write(des)
st.write("\n\n")
st.write(hnd)
st.write("\n\n\n")
st.subheader(f"Precautions :")
st.write(pre)
st.write("\n\n")
st.write(hnp)
except:
with col2:
st.subheader(":red[Enter Valid Input]")
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