add the gradion interface

README.md

@@ -10,4 +10,32 @@ pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Ad Comments Intent Classifier
+
+This Space provides an interface for classifying the intent of comments related to advertisements using the `YosefA/adfluence-intent-model`.
+
+## Features
+
+- 🎯 **Intent Classification**: Analyze comment text to determine the underlying intent
+- 📊 **Confidence Scores**: Get probability scores for each predicted label
+- 💡 **Easy to Use**: Simple interface with example comments provided
+- ⚡ **Fast Inference**: Optimized for quick classification results
+
+## How to Use
+
+1. Enter your comment text in the input box
+2. Click "🔍 Classify Intent" or press Enter
+3. View the classification results with confidence scores
+
+## Model Information
+
+This app uses the `YosefA/adfluence-intent-model` from Hugging Face, which is trained to classify the intent of comments in advertising contexts.
+
+## Examples
+
+Try these example comments to see how the classifier works:
+- "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." 

app.py

@@ -0,0 +1,111 @@
+import gradio as gr
+from transformers import pipeline
+import torch
+
+# Load the model from Hugging Face
+@gr.cache
+def load_model():
+    """Load the intent classification model"""
+    try:
+        classifier = pipeline(
+            "text-classification",
+            model="YosefA/adfluence-intent-model",
+            return_all_scores=True
+        )
+        return classifier
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None
+
+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() 

requirements.txt

@@ -0,0 +1,5 @@
+gradio==4.44.0
+transformers==4.36.0
+torch==2.1.0
+tokenizers==0.15.0
+huggingface_hub==0.19.4