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import torch
import gradio as gr
from sentence_transformers import SentenceTransformer
from safetensors.torch import load_file
import torch.nn as nn
# Define the model class (same as in the training script)
class Magical1Sun(nn.Module):
def __init__(self, num_classes, dropout_rate=0.1):
super(Magical1Sun, self).__init__()
self.sentence_transformer = SentenceTransformer('all-MiniLM-L12-v2')
self.dropout = nn.Dropout(dropout_rate)
self.classifier = nn.Sequential(
nn.Linear(384, 256),
nn.ReLU(),
nn.Dropout(dropout_rate),
nn.Linear(256, num_classes)
)
def forward(self, text):
embeddings = self.sentence_transformer.encode(text, convert_to_tensor=True)
embeddings = self.dropout(embeddings)
return self.classifier(embeddings)
# Load the trained model
def load_model(model_path):
model = Magical1Sun(num_classes=2)
state_dict = load_file(model_path)
model.load_state_dict(state_dict)
model.eval()
return model
# Prediction function
def predict(text):
with torch.no_grad():
output = model(text)
probabilities = torch.softmax(output, dim=0)
positive_prob = probabilities[1].item()
negative_prob = probabilities[0].item()
prediction = "Positive" if positive_prob > negative_prob else "Negative"
confidence = max(positive_prob, negative_prob)
return {
"Prediction": prediction,
"Confidence": f"{confidence:.2%}",
"Positive Probability": f"{positive_prob:.2%}",
"Negative Probability": f"{negative_prob:.2%}"
}
# Load the model (make sure to replace 'path_to_your_model.safetensors' with the actual path)
model = load_model('magical_1_sun.safetensors')
# Create the Gradio interface
iface = gr.Interface(
fn=predict,
inputs=gr.Textbox(lines=3, placeholder="Enter text to classify..."),
outputs=[
gr.Label(num_top_classes=1, label="Prediction"),
gr.Label(label="Confidence"),
gr.Label(label="Positive Probability"),
gr.Label(label="Negative Probability")
],
title="Magical-1 Sun Text Classification",
description="Enter a text to classify it as positive or negative.",
examples=[
["I love this product! It's amazing!"],
["This is terrible. Worst purchase ever."],
["Great experience overall. Would buy again."],
["Never buying again. Complete waste of money."],
["Highly recommended! You won't regret it."]
]
)
# Launch the app
if __name__ == "__main__":
iface.launch() |