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NVASAIKUMAR commited on
Commit
b756fab
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1 Parent(s): b2d1e49

Update model.py

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Files changed (1) hide show
  1. model.py +5 -5
model.py CHANGED
@@ -31,7 +31,7 @@ def classify(img):
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  def bone_net(img):
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  img = cv2.resize(img,(224,224))
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- model = tf.keras.models.load_model("Fracture_detection.h5",compile=False)
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  result = model.predict(np.array([img]))
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  op=""
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  if result[0]<0.5:
@@ -51,7 +51,7 @@ def brain_net(img):
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  def chest_net(img):
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  lt = ['PNEUMONIA', 'NORMAL']
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  img = cv2.resize(img,(224,224))
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- model = tf.keras.models.load_model("chest_cls_model.h5",compile=False)
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  result = model.predict(np.array([img]))
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  ans = np.argmax(result)
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  return lt[ans]
@@ -59,7 +59,7 @@ def chest_net(img):
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  def Eye_net(img):
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  lt = ['glaucoma', 'normal', 'diabetic_retinopathy', 'cataract']
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  img = cv2.resize(img,(224,224))
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- model = tf.keras.models.load_model("Eye_diseases.h5",compile=False)
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  result = model.predict(np.array([img]))
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  ans = np.argmax(result)
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  return lt[ans]
@@ -67,7 +67,7 @@ def Eye_net(img):
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  def kidney_net(img):
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  lt = ['Cyst', 'Tumor', 'Stone', 'Normal']
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  img = cv2.resize(img,(224,224))
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- model = tf.keras.models.load_model("kidney_stone.h5",compile=False)
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  result = model.predict(np.array([img]))
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  ans = np.argmax(result)
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  return lt[ans]
@@ -75,7 +75,7 @@ def kidney_net(img):
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  def skin_net(img):
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  lt = ['pigmented benign keratosis', 'melanoma', 'vascular lesion', 'actinic keratosis', 'squamous cell carcinoma', 'basal cell carcinoma', 'seborrheic keratosis', 'dermatofibroma', 'nevus']
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  img = cv2.resize(img,(224,224))
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- model = tf.keras.models.load_model("skin_cancer.h5",compile=False)
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  result = model.predict(np.array([img]))
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  ans = np.argmax(result)
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  return lt[ans]
 
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  def bone_net(img):
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  img = cv2.resize(img,(224,224))
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+ model = tf.keras.models.load_model("Fracture.h5",compile=False)
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  result = model.predict(np.array([img]))
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  op=""
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  if result[0]<0.5:
 
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  def chest_net(img):
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  lt = ['PNEUMONIA', 'NORMAL']
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  img = cv2.resize(img,(224,224))
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+ model = tf.keras.models.load_model("chest.h5",compile=False)
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  result = model.predict(np.array([img]))
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  ans = np.argmax(result)
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  return lt[ans]
 
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  def Eye_net(img):
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  lt = ['glaucoma', 'normal', 'diabetic_retinopathy', 'cataract']
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  img = cv2.resize(img,(224,224))
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+ model = tf.keras.models.load_model("eye.h5",compile=False)
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  result = model.predict(np.array([img]))
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  ans = np.argmax(result)
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  return lt[ans]
 
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  def kidney_net(img):
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  lt = ['Cyst', 'Tumor', 'Stone', 'Normal']
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  img = cv2.resize(img,(224,224))
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+ model = tf.keras.models.load_model("kidney.h5",compile=False)
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  result = model.predict(np.array([img]))
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  ans = np.argmax(result)
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  return lt[ans]
 
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  def skin_net(img):
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  lt = ['pigmented benign keratosis', 'melanoma', 'vascular lesion', 'actinic keratosis', 'squamous cell carcinoma', 'basal cell carcinoma', 'seborrheic keratosis', 'dermatofibroma', 'nevus']
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  img = cv2.resize(img,(224,224))
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+ model = tf.keras.models.load_model("skin.h5",compile=False)
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  result = model.predict(np.array([img]))
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  ans = np.argmax(result)
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  return lt[ans]