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Update app.py
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app.py
CHANGED
@@ -17,7 +17,7 @@ def load_agent(model_id_1, model_id_2):
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results_1 = parse_metrics_accuracy(metadata_1)
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# Load the video
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video_path_1 = hf_hub_download(model_id_1, filename="replay.mp4")
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# Load the metrics
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metadata_2 = get_metadata(model_id_2)
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@@ -26,9 +26,9 @@ def load_agent(model_id_1, model_id_2):
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results_2 = parse_metrics_accuracy(metadata_2)
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# Load the video
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video_path_2 = hf_hub_download(model_id_2, filename="replay.mp4")
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return model_id_1
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def parse_metrics_accuracy(meta):
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if "model-index" not in meta:
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@@ -58,7 +58,7 @@ def get_metadata(model_id):
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with app:
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gr.Markdown(
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"""
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# Compare
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Type two models id you want to compare or check examples below.
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""")
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@@ -70,19 +70,17 @@ with app:
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with gr.Row():
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with gr.Column():
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model1_name = gr.Markdown()
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model1_video_output = gr.Video()
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model1_score_output = gr.Textbox(label="Mean Reward +/- Std Reward")
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with gr.Column():
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model2_name = gr.Markdown()
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model2_video_output = gr.Video()
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model2_score_output = gr.Textbox(label="Mean Reward +/- Std Reward")
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app_button.click(load_agent, inputs=[model1_input, model2_input], outputs=[model1_name,
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examples = gr.Examples(examples=[["
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["
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["sb3/dqn-SpaceInvadersNoFrameskip-v4", "sb3/a2c-SpaceInvadersNoFrameskip-v4"],
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["ThomasSimonini/ppo-QbertNoFrameskip-v4","sb3/ppo-QbertNoFrameskip-v4"]],
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inputs=[model1_input, model2_input])
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results_1 = parse_metrics_accuracy(metadata_1)
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# Load the video
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#video_path_1 = hf_hub_download(model_id_1, filename="replay.mp4")
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# Load the metrics
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metadata_2 = get_metadata(model_id_2)
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results_2 = parse_metrics_accuracy(metadata_2)
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# Load the video
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#video_path_2 = hf_hub_download(model_id_2, filename="replay.mp4")
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return model_id_1 results_1, model_id_2, results_2
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def parse_metrics_accuracy(meta):
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if "model-index" not in meta:
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with app:
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gr.Markdown(
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"""
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# Compare Sentiment Analysis Models
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Type two models id you want to compare or check examples below.
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""")
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with gr.Row():
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with gr.Column():
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model1_name = gr.Markdown()
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#model1_video_output = gr.Video()
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model1_score_output = gr.Textbox(label="Mean Reward +/- Std Reward")
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with gr.Column():
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model2_name = gr.Markdown()
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#model2_video_output = gr.Video()
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model2_score_output = gr.Textbox(label="Mean Reward +/- Std Reward")
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app_button.click(load_agent, inputs=[model1_input, model2_input], outputs=[model1_name, model1_score_output, model2_name, model2_score_output])
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examples = gr.Examples(examples=[["scikit-learn/sentiment-analysis","microsoft/Multilingual-MiniLM-L12-H384"],
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["distilbert-base-uncased-finetuned-sst-2-english", "microsoft/Multilingual-MiniLM-L12-H384"],
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inputs=[model1_input, model2_input])
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