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Update app.py
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app.py
CHANGED
@@ -11,24 +11,6 @@ from transformers import EvalPrediction
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import torch
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import gradio as gr
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# REMOVE THIS IN COLAB
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title = 'Text market sentiment'
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text_ = "Bitcoin to the moon"
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model = torch.load("./model.pt", map_location=torch.device('cpu'))
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inp = [gr.Textbox(label='API Key', placeholder="What is your API Key?"), gr.Textbox(label='Audio File URL', placeholder="Audio file URL?")]
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out = gr.Textbox(label='Output')
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text_button = gr.Button("Flip")
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text_button.click(audio_to_text, inputs=inp, outputs=out)
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interface = gr.Interface.load(input=inp,output=out,
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title = title,
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theme = "peach",
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examples = [[text_]]).launch()
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###############
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console = Console()
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dataset = load_dataset("zeroshot/twitter-financial-news-sentiment", )
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@@ -131,6 +113,52 @@ def compute_metrics(p: EvalPrediction):
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return result
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trainer = Trainer(
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model,
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args,
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import torch
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import gradio as gr
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console = Console()
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dataset = load_dataset("zeroshot/twitter-financial-news-sentiment", )
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return result
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# REMOVE THIS IN COLAB #############
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title = 'Text market sentiment'
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text_ = "Bitcoin to the moon"
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model = torch.load("./model.pt", map_location=torch.device('cpu'))
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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text = "Bitcoin to the moon"
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encoding = tokenizer(text, return_tensors="pt")
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# apply sigmoid + threshold
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sigmoid = torch.nn.Sigmoid()
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probs = sigmoid(logits.squeeze().cpu())
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predictions = np.zeros(probs.shape)
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predictions[np.where(probs >= 0.5)] = 1
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# turn predicted id's into actual label names
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predicted_labels = [id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
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console.log("a")
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console.log(predicted_labels)
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console.log("a")
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inp = [gr.Textbox(label='API Key', placeholder="What is your API Key?"), gr.Textbox(label='Audio File URL', placeholder="Audio file URL?")]
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out = gr.Textbox(label='Output')
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text_button = gr.Button("Flip")
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text_button.click(audio_to_text, inputs=inp, outputs=out)
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interface = gr.Interface.load(input=inp,output=out,
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title = title,
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theme = "peach",
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examples = [[text_]]).launch()
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###############
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trainer = Trainer(
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model,
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args,
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