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Browse files- app.py +36 -0
- requirements.txt +2 -0
app.py
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import gradio as gr
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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# Load the model and tokenizer
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model_name = "Priyanshuchaudhary2425/EmotiNet"
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Class list
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class_list = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']
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# Define the function to make predictions with your model
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def predict_emotion(text):
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
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outputs = model(**inputs)
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probabilities = outputs.logits.softmax(dim=1).tolist()[0]
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return {class_list[label]: probability for label, probability in enumerate(probabilities)}
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# Create a Gradio interface for your model
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output_probabilities = gr.Label(num_top_classes=6)
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interface = gr.Interface(
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fn=predict_emotion,
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inputs=gr.Textbox(lines=5, label="Enter your text here"),
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outputs=output_probabilities,
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title="Emotion Detection",
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description="This model predicts the probabilities of different emotions (sadness, joy, love, anger, fear, surprise) based on the input text.",
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examples=[
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["In her warm embrace, I found solace, a refuge from the chaos of the world. Every beat of her heart echoed the melody of love, drawing me closer with each tender touch."],
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["Fury surged through my veins, a tempest of resentment and indignation, fueled by the betrayal of trust. In that moment, every word spoken was a dagger, piercing through the facade of civility."],
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["Tears silently traced their path down my cheeks, carrying the weight of unspoken sorrows, each drop a testament to the pain within. In the quiet of the night, I grappled with the emptiness that engulfed my soul, longing for the light of hope to pierce through the darkness."]
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]
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)
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# Launch the Gradio interface with sharing enabled
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interface.launch(share=True)
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requirements.txt
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gradio==4.13.0
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transformers==4.37.1
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