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app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "Priyanshuchaudhary2425/EmotiNet"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Class list
+class_list = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']
+
+# Define the function to make predictions with your model
+def predict_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    probabilities = outputs.logits.softmax(dim=1).tolist()[0]
+    return {class_list[label]: probability for label, probability in enumerate(probabilities)}
+
+# Create a Gradio interface for your model
+output_probabilities = gr.Label(num_top_classes=6)
+
+interface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(lines=5, label="Enter your text here"),
+    outputs=output_probabilities,
+    title="Emotion Detection",
+    description="This model predicts the probabilities of different emotions (sadness, joy, love, anger, fear, surprise) based on the input text.",
+    examples=[
+        ["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."],
+        ["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."],
+        ["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."]
+    ]
+)
+
+# Launch the Gradio interface with sharing enabled
+interface.launch(share=True)

requirements.txt

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+gradio==4.13.0
+transformers==4.37.1