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import gradio as gr
from transformers import pipeline
import torch
# Global variable to cache the model
_classifier = None
def load_model():
"""Load the intent classification model"""
global _classifier
if _classifier is None:
model_name = "YosefA/adfluence-intent-model"
# Try multiple approaches to load the model
loading_strategies = [
{
"name": "Standard loading with trust_remote_code",
"kwargs": {"trust_remote_code": True, "return_all_scores": True}
},
{
"name": "Loading with revision='main'",
"kwargs": {"revision": "main", "return_all_scores": True}
},
{
"name": "Loading with use_fast=False",
"kwargs": {"use_fast": False, "return_all_scores": True}
},
{
"name": "Loading with legacy tokenizer",
"kwargs": {"use_fast": False, "trust_remote_code": True, "return_all_scores": True}
}
]
for strategy in loading_strategies:
try:
print(f"Trying: {strategy['name']}")
_classifier = pipeline(
"text-classification",
model=model_name,
**strategy['kwargs']
)
print(f"✅ Model loaded successfully using: {strategy['name']}")
return _classifier
except Exception as e:
print(f"❌ Failed with {strategy['name']}: {e}")
continue
print("❌ All loading strategies failed")
return None
return _classifier
def classify_intent(comment):
"""
Classify the intent of a comment
Args:
comment (str): The input comment text
Returns:
dict: Classification results with labels and scores
"""
if not comment.strip():
return "Please enter a comment to classify."
classifier = load_model()
if classifier is None:
return "Error: Could not load the model. Please try again later."
try:
# Get predictions
results = classifier(comment)
# Format results for display
formatted_results = []
for result in results:
for item in result:
label = item['label']
score = item['score']
formatted_results.append(f"{label}: {score:.4f} ({score*100:.2f}%)")
return "\n".join(formatted_results)
except Exception as e:
return f"Error during classification: {str(e)}"
# Create the Gradio interface
with gr.Blocks(title="Ad Comments Intent Classifier") as demo:
gr.Markdown("""
# 🎯 Ad Comments Intent Classifier
This app classifies the intent of comments related to advertisements using the **YosefA/adfluence-intent-model**.
Simply enter a comment below and get the classification results with confidence scores.
""")
with gr.Row():
with gr.Column():
comment_input = gr.Textbox(
label="Comment Text",
placeholder="Enter your comment here...",
lines=3,
max_lines=10
)
classify_btn = gr.Button("🔍 Classify Intent", variant="primary")
with gr.Column():
output = gr.Textbox(
label="Classification Results",
lines=5,
max_lines=10,
interactive=False
)
# Example inputs
gr.Examples(
examples=[
["This product looks amazing! Where can I buy it?"],
["This is clearly a scam, don't trust it."],
["I love this brand, they make quality products."],
["The price seems too high for what you get."],
["Has anyone tried this? I'm curious about reviews."]
],
inputs=comment_input,
label="📝 Example Comments"
)
# Set up the event handlers
classify_btn.click(
fn=classify_intent,
inputs=comment_input,
outputs=output
)
comment_input.submit(
fn=classify_intent,
inputs=comment_input,
outputs=output
)
# Launch the app
if __name__ == "__main__":
demo.launch() |