Vicky000 commited on
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831a54b
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Update app.py

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  1. app.py +87 -14
app.py CHANGED
@@ -4,25 +4,42 @@ import requests
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  import streamlit as st
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  """
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- # HeartNet
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- This is a classifier for images of 12-lead EKGs. It will attempt to detect whether the EKG indicates an acute MI. It was trained on simulated images.
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  """
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- def predict(img):
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- st.image(img, caption="Your image", use_column_width=True)
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- pred, key, probs = learn_inf.predict(img)
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- # st.write(learn_inf.predict(img))
 
 
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- f"""
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- ## This **{'is cordana' if pred == 'cordana' else 'is pestalotiopsis' if pred == 'pestalotiopsis' else 'is sigatoka' if pred == 'sigatoka' else 'is healthy'}**.
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- ### Prediction result: {pred}
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- ### Probability of {pred}: {probs[key].item()*100: .2f}%
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- """
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- img1 = Image.open('/home/user/app/img1.jpg')
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- img2 = Image.open('/home/user/app/img2.jpg')
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- img3 = Image.open('/home/user/app/img3.jpg')
 
 
 
 
 
 
 
 
 
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  col1, col2, col3 = st.columns(3)
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@@ -33,6 +50,62 @@ with col2:
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  with col3:
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  st.image(img3, caption="Image 3", use_column_width=True)
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  path = "./"
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  learn_inf = load_learner(path + "demo_model.pkl")
 
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  import streamlit as st
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  """
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+ # 香蕉病害分類
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+
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  """
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+ """
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+ ## 健康香蕉葉示例
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+ """
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+ img1 = Image.open('/home/user/app/healthy_1.jpg')
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+ img2 = Image.open('/home/user/app/healthy_2.jpg')
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+ img3 = Image.open('/home/user/app/healthy_3.jpg')
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+ col1, col2, col3 = st.columns(3)
 
 
 
 
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+ with col1:
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+ st.image(img1, caption="Image 1", use_column_width=True)
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+ with col2:
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+ st.image(img2, caption="Image 2", use_column_width=True)
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+ with col3:
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+ st.image(img3, caption="Image 3", use_column_width=True)
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+
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+ """
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+ # 香蕉葉斑病(Black sigatoka;Black leaf streak)
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+ 病原菌學名:Mycosphaerella fijiensis (= M. fijiensis var. fijiensis )
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+ 目前仍被視為危害全球香蕉產區最嚴重之病害,對香蕉作物之生存威脅至鉅。此病不但直接危害蕉株葉片,影響蕉株健葉數,導致產期延後或減產。
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+ 亦可間接造成香蕉外銷船運期間,因採收株葉片數不足,引發蕉果果齡偏高過熟而黃化廢棄。最早於1963年被報導發生於斐濟,國內最早記載於1927年,但缺乏考證。
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+ 1960年代在國內蕉區大流行,曾造成相當嚴重之損失,惟自1978年賽洛瑪颱風,高屏蕉區被夷為平地後,舊蕉園葉斑病菌初期感染源密度明顯降低,
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+ 加上全面執行葉部病害防治作業後,葉斑病發生地區由屏東往高雄旗山主產區逐年減少,目前僅侷限於臺灣東半部台東關山以北至花蓮壽豐之間,在西半部僅零星發生於高雄美濃至台南楠西一帶。
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+
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+ 病徵:初期病徵通常出現在第3或第4片葉背面,為紅棕色小條斑,大約5~10 ×0.1~1公厘,與葉脈平行,通常集中在葉片左側和葉尖部位。
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+ 之後條斑擴大變黑,同時亦出現在葉表面。至中期條斑擴大而呈橢圓形褐斑,周圍有黃色暈圈。至後期轉呈黑褐色或黑色病斑,而後病斑中間開始變灰色。受害葉片提早枯死。
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+
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+ """
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+ img1 = Image.open('/home/user/app/sigatoka_1.jpg')
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+ img2 = Image.open('/home/user/app/sigatoka_2.jpg')
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+ img3 = Image.open('/home/user/app/sigatoka_3.jpg')
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  col1, col2, col3 = st.columns(3)
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  with col3:
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  st.image(img3, caption="Image 3", use_column_width=True)
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+ """
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+ # 圓星病(Cordana leaf spot)
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+ 病原菌學名:Cordana musae(Zimm.)Hohnel
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+
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+ 分布極廣,但屬輕微葉部病害。當葉片弱化、老化、處逆境、營養不良、有傷口或受其他生物感染時,危害較明顯。
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+ 病斑大,橢圓形,呈同心圓,淡褐色或黃色。病斑中心為灰色、周圍繞有鮮黃色暈圈;
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+
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+ 病斑有時會融合擴大,或在葉片邊緣銜接而使整個葉緣枯掉,本病通常只在老葉發生。
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+ 防治方法:葉部黑星病防治藥劑對本病皆有防治效果。
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+ """
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+ img1 = Image.open('/home/user/app/cordana_1.jpg')
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+ img2 = Image.open('/home/user/app/cordana_2.jpg')
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+ img3 = Image.open('/home/user/app/cordana_3.jpg')
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+
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+ col1, col2, col3 = st.columns(3)
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+
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+ with col1:
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+ st.image(img1, caption="Image 1", use_column_width=True)
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+ with col2:
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+ st.image(img2, caption="Image 2", use_column_width=True)
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+ with col3:
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+ st.image(img3, caption="Image 3", use_column_width=True)
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+
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+ """
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+ # 擬盤多毛胞屬真菌病害(Pestalotiopsis)
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+ 著名的植物病原體品種,主要分布於熱帶地區,可危害多種果樹,於貯後病害紀錄如檬果、番石榴、荔枝、酪梨等皆曾發現,也常引起水果的果腐病。
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+
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+ 病徵:感染葉片則出現淡黃褐色病斑,表面散生黑色小點,此為分生孢子盤構造,常發生於破損葉片或生長勢較弱的植株;
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+ 受感染植株在新梢的修剪傷口附近可見病班,病斑表面黑色小點突起,為分生孢子盤內有分生孢子,可由表面頂點孔處釋出。
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+ """
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+
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+ img1 = Image.open('/home/user/app/pestalotiopsis_1.jpg')
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+ img2 = Image.open('/home/user/app/pestalotiopsis_2.jpg')
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+ img3 = Image.open('/home/user/app/pestalotiopsis_3.jpg')
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+
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+ col1, col2, col3 = st.columns(3)
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+
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+ with col1:
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+ st.image(img1, caption="Image 1", use_column_width=True)
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+ with col2:
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+ st.image(img2, caption="Image 2", use_column_width=True)
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+ with col3:
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+ st.image(img3, caption="Image 3", use_column_width=True)
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+
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+
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+
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+ def predict(img):
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+ st.image(img, caption="Your image", use_column_width=True)
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+ pred, key, probs = learn_inf.predict(img)
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+ # st.write(learn_inf.predict(img))
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+
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+ f"""
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+ ## This **{'is cordana' if pred == 'cordana' else 'is pestalotiopsis' if pred == 'pestalotiopsis' else 'is sigatoka' if pred == 'sigatoka' else 'is healthy'}** banana leaf.
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+ ### Prediction result: {pred}
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+ ### Probability of {pred}: {probs[key].item()*100: .2f}%
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+ """
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  path = "./"
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  learn_inf = load_learner(path + "demo_model.pkl")