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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() |