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Update app.py
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app.py
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# https://huggingface.co/spaces/CK42/sentiment-model-comparison/blob/main/app.py
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# import sklearn
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from os import O_ACCMODE
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import gradio as gr
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return metadata
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except requests.exceptions.HTTPError:
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return None
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# classifier = pipeline("text-classification", model="juliensimon/distilbert-amazon-shoe-reviews")
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def predict(review, model_id):
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classifier = pipeline("text-classification", model=model_id)
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inp_1= gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")
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out_2 = gr.Textbox(label="Prediction")
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# """)
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with gr.Row():
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model1_input = gr.Textbox(label="Model 1")
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with gr.Row():
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model2_input = gr.Textbox(label="Model 2")
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with gr.Row():
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btn = gr.Button("Prediction for Model 2")
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btn.click(fn=
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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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# import sklearn
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from os import O_ACCMODE
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import gradio as gr
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return metadata
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except requests.exceptions.HTTPError:
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return None
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def predict(review, model_id):
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classifier = pipeline("text-classification", model=model_id)
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inp_1= gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")
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out_2 = gr.Textbox(label="Prediction")
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gr.Markdown(
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"""
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Model Predictions
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""")
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with gr.Row():
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model1_input = gr.Textbox(label="Model 1")
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with gr.Row():
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model2_input = gr.Textbox(label="Model 2")
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with gr.Row():
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btn = gr.Button("Prediction for Model 2")
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btn.click(fn=load_agent(model_id_2), inputs=inp_1, outputs=out_2)
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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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