prithivMLmods commited on
Commit
fa4176e
·
verified ·
1 Parent(s): 096ed03

Update app.py

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Files changed (1) hide show
  1. app.py +10 -2
app.py CHANGED
@@ -3,7 +3,8 @@ from gender_classification import gender_classification
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  from emotion_classification import emotion_classification
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  from dog_breed import dog_breed_classification
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  from deepfake_vs_real import deepfake_classification
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- from gym_workout_classification import workout_classification
 
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  # Functions to update the model state when a button is clicked.
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  def select_gender():
@@ -21,6 +22,9 @@ def select_deepfake():
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  def select_gym_workout():
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  return "gym workout"
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  # Main classification function that calls the appropriate model based on selection.
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  def classify(image, model_name):
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  if model_name == "gender":
@@ -33,19 +37,22 @@ def classify(image, model_name):
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  return deepfake_classification(image)
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  elif model_name == "gym workout":
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  return gym_workout_classification(image)
 
 
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  else:
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  return {"Error": "No model selected"}
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  with gr.Blocks() as demo:
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  # Sidebar with title and model selection buttons.
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  with gr.Sidebar():
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- gr.Markdown("# SigLIP2 Classification")
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  with gr.Row():
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  gender_btn = gr.Button("Gender Classification")
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  emotion_btn = gr.Button("Emotion Classification")
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  dog_breed_btn = gr.Button("Dog Breed Classification")
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  deepfake_btn = gr.Button("Deepfake vs Real")
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  gym_workout_btn = gr.Button("Gym Workout Classification")
 
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  # State to hold the current model choice.
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  selected_model = gr.State("gender")
@@ -56,6 +63,7 @@ with gr.Blocks() as demo:
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  dog_breed_btn.click(fn=select_dog_breed, inputs=[], outputs=selected_model)
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  deepfake_btn.click(fn=select_deepfake, inputs=[], outputs=selected_model)
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  gym_workout_btn.click(fn=select_gym_workout, inputs=[], outputs=selected_model)
 
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  gr.Markdown("### Current Model:")
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  model_display = gr.Textbox(value="gender", interactive=False)
 
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  from emotion_classification import emotion_classification
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  from dog_breed import dog_breed_classification
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  from deepfake_vs_real import deepfake_classification
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+ from gym_workout_classification import gym_workout_classification
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+ from augmented_waste_classifier import waste_classification
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  # Functions to update the model state when a button is clicked.
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  def select_gender():
 
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  def select_gym_workout():
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  return "gym workout"
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+ def select_waste():
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+ return "waste"
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+
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  # Main classification function that calls the appropriate model based on selection.
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  def classify(image, model_name):
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  if model_name == "gender":
 
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  return deepfake_classification(image)
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  elif model_name == "gym workout":
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  return gym_workout_classification(image)
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+ elif model_name == "waste":
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+ return waste_classification(image)
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  else:
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  return {"Error": "No model selected"}
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  with gr.Blocks() as demo:
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  # Sidebar with title and model selection buttons.
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  with gr.Sidebar():
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+ gr.Markdown("# SigLIP2 224")
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  with gr.Row():
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  gender_btn = gr.Button("Gender Classification")
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  emotion_btn = gr.Button("Emotion Classification")
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  dog_breed_btn = gr.Button("Dog Breed Classification")
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  deepfake_btn = gr.Button("Deepfake vs Real")
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  gym_workout_btn = gr.Button("Gym Workout Classification")
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+ waste_btn = gr.Button("Waste Classification")
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  # State to hold the current model choice.
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  selected_model = gr.State("gender")
 
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  dog_breed_btn.click(fn=select_dog_breed, inputs=[], outputs=selected_model)
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  deepfake_btn.click(fn=select_deepfake, inputs=[], outputs=selected_model)
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  gym_workout_btn.click(fn=select_gym_workout, inputs=[], outputs=selected_model)
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+ waste_btn.click(fn=select_waste, inputs=[], outputs=selected_model)
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  gr.Markdown("### Current Model:")
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  model_display = gr.Textbox(value="gender", interactive=False)