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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_path = "arad1367/crypto_sustainability_news_FacebookAI_roberta-large-mnli"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
def crypto_classifier(text: str):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
labels = ["Negative", "Neutral", "Positive"]
output_dict = {label: prob.item() for label, prob in zip(labels, probabilities[0])}
return output_dict
custom_css = """
.container {
max-width: 1200px;
margin: auto;
padding: 20px;
font-family: 'Inter', system-ui, -apple-system, sans-serif;
}
.header {
text-align: center;
margin: 2em 0;
color: #2d7ff9;
}
.description {
text-align: center;
margin-bottom: 2em;
color: #666;
}
.footer {
text-align: center;
margin-top: 20px;
padding: 20px;
border-top: 1px solid #eee;
background: #f8f9fa;
}
.footer a {
color: #2d7ff9;
text-decoration: none;
margin: 0 10px;
font-weight: 500;
}
.footer a:hover {
text-decoration: underline;
}
.duplicate-button {
background-color: #2d7ff9 !important;
color: white !important;
border-radius: 8px !important;
padding: 10px 20px !important;
margin: 20px auto !important;
display: block !important;
}
"""
examples = [
["The Crypto Conference, focusing on sustainable crypto, will be organized next year."],
["There are growing concerns about the environmental impact of cryptocurrency mining processes."],
["The new blockchain protocol reduces energy consumption by 90%."],
["The decentralized network operates on renewable energy sources."],
["Bitcoin mining contributes to increased carbon emissions in developing countries."]
]
with gr.Blocks(theme='earneleh/paris', css=custom_css) as demo:
with gr.Column(elem_classes="container"):
gr.Markdown("# Cryptocurrency News Sustainability Classifier", elem_classes="header")
gr.Markdown(
"Analyze cryptocurrency-related text to determine its sustainability implications.",
elem_classes="description"
)
input_text = gr.Textbox(
label="Input Text",
placeholder="Enter cryptocurrency-related news or statement...",
lines=3
)
output_label = gr.Label(label="Classification Results", num_top_classes=3)
submit_btn = gr.Button("Analyze", variant="primary")
gr.Examples(examples=examples, inputs=input_text)
submit_btn.click(fn=crypto_classifier, inputs=input_text, outputs=output_label)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_classes="duplicate-button"
)
gr.HTML("""
<div class="footer">
<a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> |
<a href="https://github.com/arad1367" target="_blank">GitHub</a> |
<a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a>
<p>Made with 💖 by Pejman Ebrahimi</p>
</div>
""")
demo.launch(share=True) |