Update app.py
Browse files
app.py
CHANGED
@@ -10,8 +10,6 @@ import cnnrnn
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from build_vocab import Vocabulary
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# Caption generation functions
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def generate_caption_clipgpt(image, max_tokens, temperature):
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caption = clipGPT.generate_caption_clipgpt(image, max_tokens, temperature)
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@@ -35,12 +33,10 @@ def generate_caption_cnnrnn(image):
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with gr.Row():
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image = gr.Image(label="Upload Chest X-ray", type="pil")
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with gr.Row():
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with gr.Column(): # Column for dropdowns and model choice
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max_tokens = gr.Dropdown(list(range(50, 101)), label="Max Tokens", value=75)
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temperature = gr.Slider(0.5, 0.9, step=0.1, label="Temperature", value=0.7)
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@@ -59,7 +55,7 @@ def predict(img, model_name, max_tokens, temperature):
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elif model_name == "ViT-CoAttention":
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return generate_caption_vitCoAtt(img)
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elif model_name == "Baseline Model CNN-RNN":
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print(img)
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return generate_caption_cnnrnn(img)
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else:
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return "Caption generation for this model is not yet implemented."
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@@ -68,6 +64,7 @@ def predict(img, model_name, max_tokens, temperature):
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examples = [[f"example{i}.jpg"] for i in range(1,4)]
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description= "You can generate captions by uploading an X-Ray and selecting a model of your choice below. Please select the number of Max Tokens and Temperature setting, if you are testing CLIP GPT2 and VIT GPT2 Models"
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title = "MedViT: A Vision Transformer-Driven Method for Generating Medical Reports π₯π€"
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from build_vocab import Vocabulary
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# Caption generation functions
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def generate_caption_clipgpt(image, max_tokens, temperature):
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caption = clipGPT.generate_caption_clipgpt(image, max_tokens, temperature)
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with gr.Row():
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image = gr.Image(label="Upload Chest X-ray", type="pil")
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with gr.Row():
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with gr.Column(): # Column for dropdowns and model choice
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max_tokens = gr.Dropdown(list(range(50, 101)), label="Max Tokens", value=75)
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temperature = gr.Slider(0.5, 0.9, step=0.1, label="Temperature", value=0.7)
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elif model_name == "ViT-CoAttention":
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return generate_caption_vitCoAtt(img)
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elif model_name == "Baseline Model CNN-RNN":
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print(img.name)
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return generate_caption_cnnrnn(img)
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else:
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return "Caption generation for this model is not yet implemented."
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examples = [[f"example{i}.jpg"] for i in range(1,4)]
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description= "You can generate captions by uploading an X-Ray and selecting a model of your choice below. Please select the number of Max Tokens and Temperature setting, if you are testing CLIP GPT2 and VIT GPT2 Models"
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title = "MedViT: A Vision Transformer-Driven Method for Generating Medical Reports π₯π€"
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