Soham Chandratre commited on
Commit
c4e1faf
·
1 Parent(s): 6750185

minor changes

Browse files
model/__pycache__/pothole_model.cpython-311.pyc CHANGED
Binary files a/model/__pycache__/pothole_model.cpython-311.pyc and b/model/__pycache__/pothole_model.cpython-311.pyc differ
 
model/pothole_model.py CHANGED
@@ -23,13 +23,11 @@
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  # return predicted_class
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-
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- from keras.models import load_model
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  from PIL import Image, ImageOps
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  import numpy as np
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  import requests
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- import tempfile
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- import os
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  def load_image_model(image):
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  # Disable scientific notation for clarity
@@ -39,14 +37,10 @@ def load_image_model(image):
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  model_url = "https://huggingface.co/spaces/Soham0708/pothole_detect/blob/main/keras_model.h5"
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  response = requests.get(model_url)
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  response.raise_for_status() # Raise an exception if the download fails
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-
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- # Save the model to a temporary file
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- with tempfile.NamedTemporaryFile(suffix=".h5", delete=False) as tmp_file:
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- tmp_file.write(response.content)
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- tmp_file_path = tmp_file.name
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- # Load the model from the temporary file
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- model = load_model(tmp_file_path)
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  # Load the labels
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  class_names = open("labels.txt", "r").readlines()
@@ -57,7 +51,7 @@ def load_image_model(image):
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  data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
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  # Replace this with the path to your image
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- image = Image.open(by(image)).convert("RGB")
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  # resizing the image to be at least 224x224 and then cropping from the center
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  size = (224, 224)
@@ -80,7 +74,4 @@ def load_image_model(image):
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  # Print prediction and confidence score
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  print("Class:", class_name[2:], end="")
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- print("Confidence Score:", confidence_score)
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-
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- # Clean up temporary file
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- os.remove(tmp_file_path)
 
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  # return predicted_class
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+ import tensorflow as tf
 
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  from PIL import Image, ImageOps
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  import numpy as np
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  import requests
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+ from io import BytesIO
 
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  def load_image_model(image):
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  # Disable scientific notation for clarity
 
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  model_url = "https://huggingface.co/spaces/Soham0708/pothole_detect/blob/main/keras_model.h5"
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  response = requests.get(model_url)
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  response.raise_for_status() # Raise an exception if the download fails
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+ model_data = BytesIO(response.content)
 
 
 
 
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+ # Load the model from the in-memory bytes
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+ model = tf.keras.models.load_model(model_data)
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  # Load the labels
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  class_names = open("labels.txt", "r").readlines()
 
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  data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
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  # Replace this with the path to your image
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+ image = Image.open(image).convert("RGB")
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  # resizing the image to be at least 224x224 and then cropping from the center
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  size = (224, 224)
 
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  # Print prediction and confidence score
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  print("Class:", class_name[2:], end="")
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+ print("Confidence Score:", confidence_score)