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Update app.py
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app.py
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@@ -1,6 +1,7 @@
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import sklearn
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import gradio as gr
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import joblib
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import requests.exceptions
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from huggingface_hub import HfApi, hf_hub_download
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from huggingface_hub.repocard import metadata_load
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@@ -9,11 +10,12 @@ from huggingface_hub.repocard import metadata_load
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#inputs = [gr.Textbox(value = "The customer service was satisfactory.")]
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#outputs = [gr.Label(label = "Sentiment")]
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#title = "Sentiment Analysis"
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app = gr.Blocks()
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def load_agent(model_id_1, model_id_2):
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"""
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This function load the agent's
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:return: video_path
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"""
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# Load the metrics
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with gr.Row():
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model1_input = gr.Textbox(label="Model 1")
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model2_input = gr.Textbox(label="Model 2")
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with gr.Row():
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app_button = gr.Button("Compare models")
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with gr.Row():
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import sklearn
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import gradio as gr
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import joblib
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from transformers import pipeline
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import requests.exceptions
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from huggingface_hub import HfApi, hf_hub_download
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from huggingface_hub.repocard import metadata_load
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#inputs = [gr.Textbox(value = "The customer service was satisfactory.")]
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#outputs = [gr.Label(label = "Sentiment")]
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#title = "Sentiment Analysis"
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app = gr.Blocks()
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def load_agent(model_id_1, model_id_2):
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"""
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This function load the agent's results
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:return: video_path
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"""
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# Load the metrics
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with gr.Row():
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model1_input = gr.Textbox(label="Model 1")
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model2_input = gr.Textbox(label="Model 2")
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with gr.Row():
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inp = gr.Textbox(placeholder="The customer service was satisfactory.")
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out = gr.Textbox()
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btn = gr.Button("Run")
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btn.click(fn=update, inputs=inp, outputs=out)
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with gr.Row():
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app_button = gr.Button("Compare models")
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with gr.Row():
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