Update app.py

app.py

@@ -1,50 +1,3 @@
 import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
 
-# Load the model and tokenizer once during initialization
-model_name = "AnkitAI/deberta-xlarge-base-emotions-classifier"
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Define the function to use the model for predictions
-def classify_emotion(text):
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-    outputs = model(**inputs)
-    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    labels = ["joy", "anger", "sadness", "fear", "surprise", "love"]  # Adjust based on the actual labels used by the model
-    return {labels[i]: float(probs[0][i]) for i in range(len(labels))}
-
-# Validate the input
-def validate_input(text):
-    if len(text.strip()) == 0:
-        return "Please enter some text."
-    return classify_emotion(text)
-
-# Define the Gradio interface
-interface = gr.Interface(
-    fn=validate_input,
-    inputs=gr.Textbox(lines=5, placeholder="Enter text here...", label="Input Text"),
-    outputs=gr.Label(label="Predicted Emotion"),
-    title="Emotion Classifier",
-    description="Enter some text and let the model predict the emotion.",
-    examples=["I am feeling great today!", "I am so sad and depressed.", "I am excited about the new project."],
-)
-
-# Add some custom CSS to improve the look and feel
-css = """
-body {
-    background-color: #f8f9fa;
-    font-family: Arial, sans-serif;
-}
-h1 {
-    color: #007bff;
-}
-.gradio-container {
-    border-radius: 10px;
-    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
-}
-"""
-
-# Launch the Gradio app with custom CSS
-interface.launch(server_name="0.0.0.0", server_port=8080, inline=False, css=css)
+gr.load("models/AnkitAI/reviews-roberta-base-sentiment-analysis").launch()