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Chicken_Heart_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d783c67e3831d9f027248cdabec0463807c16871692e4cd6100a6e0d1341b012
+size 220570576

Liver_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f6e5ec33701c39f01880a39cc234b91f7d65c5655225513dcf8df4cc312a055
+size 234256912

Lungs_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:89b45715139e7df3c2342d82f626eb1c6b49357ecff5ddf839492da603ad5648
+size 234256912

app.py

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+from keras.models import load_model
+import cv2
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import gradio as gr
+import numpy as np
+
+
+my_model=load_model('Liver_model.h5',compile=True)
+heart_model=load_model('Chicken_Heart_model.h5',compile=True)
+lu_model=load_model('Lungs_model.h5',compile=True)
+auth_model=load_model('post_auth_model.h5',compile=True)
+heart_class_name={0:'Dilation(eccentric)',1:'Hepatoma',2:'Hypertrophy(concentric)',3:'Hypertrophy(physiological)',4:'Infraction Damage',5:'Normal'}
+heart_result={0:'Critical',1:'Critical',2:'Critical',3:'Critical',4:'Critical',5:'Normal'}
+heart_recommend={0:'panadol',1:'peracetamol',2:'ponston',4:'brofon',5:'No Need'}
+
+def Heart_Disease_prediction(img):
+  img = img.reshape((1, img.shape[0], img.shape[1], img.shape[2]))
+
+  # Create the data generator with desired properties
+  datagen = ImageDataGenerator(
+    rotation_range=30,
+    width_shift_range=0.1,
+    height_shift_range=0.1,
+    shear_range=0.1,
+    zoom_range=0.1,
+    horizontal_flip=True,
+    fill_mode="nearest",
+  )
+  # Generate a batch of augmented images (contains only the single image) 
+  augmented_images = datagen.flow(img, batch_size=1)
+  # Get the first (and only) augmented image from the batch
+  augmented_img = next(augmented_images)[0]
+  img=cv2.resize(augmented_img.astype(np.uint8),(128,128))
+  class_no=heart_model.predict(img.reshape(1,128,128,3)).argmax()
+  name="Heart Disease Class Name: "+""+heart_class_name.get(class_no) 
+  result="Heart Disease result: "+""+heart_result.get(class_no)
+  recommend="Heart Medicine Recommend: "+""+heart_recommend.get(class_no)
+  return name,result,recommend
+
+liver_class_num={0:'Healthy',1:'Un-Healthy'}
+liver_result={0:'Normal',1:'Critical'}
+liver_recommend={0:'No need Medicine',1:'Panadol'}
+def Liver_Predict(Image):
+  Image=cv2.resize(Image,(224,224))
+  class_no=my_model.predict(Image.reshape(1,224,224,3)).argmax()
+  class_name="Liver Class Name: "+""+liver_class_num.get(class_no)
+  liver_class_result="Liver Class Result: "+""+liver_result.get(class_no)
+  liver_class_recommend="Liver Class Recommend: "+""+liver_recommend.get(class_no)
+  return class_name,liver_class_result,liver_class_recommend
+
+
+
+lung_classes={0:'Lungs of infected chickens showing congestion, hemorrhage and consolidation with traces of fibrin at 24 hpi (hours post-infection)',
+              1:'gradual paleness and reduction in size of lungs at 2 dpi (days post-infection)',
+              2:'gradual paleness and reduction in size of lung at 3 dpi (days post-infection)',
+              3:'severe congestion, hemorrhage, and gradual shrinking of lungs at 4 dpi (days post-infection)',
+              4:'severe congestion, hemorrhage, and gradual shrinking of lungs at 5 dpi (days post-infection)'}
+lung_result={0:'critical',1:'critical',2:'critical',3:'critical',4:'critical'}
+lung_recommend={0:'panadol',1:'peracetamol',2:'ponston',4:'brofon'}
+
+def Lungs_predict(image):
+  image=cv2.resize(image,(224,224))
+  lung_no=lu_model.predict(image.reshape(1,224,224,3)).argmax()
+  lung_disease_name="Lung Disease Name: "+""+lung_classes.get(lung_no)
+  lung_r="Lung result: "+""+lung_result.get(lung_no)
+  lung_re="Lung recommendation: "+""+lung_recommend.get(lung_no)
+  return lung_disease_name,lung_r,lung_re
+  
+
+def main(Image):
+    img=cv2.resize(Image,(224,224))
+    indx=auth_model.predict(img.reshape(1,224,224,3)).argmax()
+    if indx==0:
+        Name='Unkown'
+        result='N/A'
+        recommend='N/A'
+        return Name,result,recommend,Name,result,recommend,Name,result,recommend
+    else:
+        heart_n,heart_r,heart_re=Heart_Disease_prediction(Image)
+        liver_name,liver_r,liver_re=Liver_Predict(Image)
+        lung_d,lung_r,lung_re=Lungs_predict(Image)
+        return heart_n,heart_r,heart_re,liver_name,liver_r,liver_re,lung_d,lung_r,lung_re
+
+interface=gr.Interface(fn=main,inputs='image',outputs=[gr.components.Textbox(label="Heart Disease Name"),gr.components.Textbox(label="Heart result Name"),gr.components.Textbox(label="Heart recommend"),
+                                                         gr.components.Textbox(label="Liver Disease Name"),gr.components.Textbox(label="liver result Name"),gr.components.Textbox(label="Liver recommend"),
+                                                         gr.components.Textbox(label="Lung Disease Name"),gr.components.Textbox(label="Lung result Name"),gr.components.Textbox(label="Lung recommend")],
+                       title="Postmortem") 
+interface.launch(debug=True)
+

post_auth_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1cf43deb182f86f93d0fd9d37ae27638c40c31038442538c6f378d164bb0d6ec
+size 215440896

requirements.txt

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+tensorflow==2.12.0
+keras
+opencv-python