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app.py

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+import gradio as gr
+from transformers import pipeline
+import re
+
+# Load your fine-tuned model
+try:
+    ner_pipeline = pipeline("ner", 
+                           model="MuneebAbro/ecommerce-ner-model",
+                           aggregation_strategy="simple")
+    model_loaded = True
+except:
+    # Fallback to a general NER model if yours isn't ready
+    ner_pipeline = pipeline("ner", 
+                           model="dbmdz/bert-large-cased-finetuned-conll03-english",
+                           aggregation_strategy="simple")
+    model_loaded = False
+
+def extract_product_info(text):
+    """Extract product information and format results"""
+    if not text.strip():
+        return "Please enter some text!"
+    
+    try:
+        # Enhanced regex patterns for better extraction
+        result = {
+            "product_name": "",
+            "brand": "",
+            "price": "",
+            "quantities": []
+        }
+        
+        # Extract brand
+        brand_patterns = [
+            r'\b(Samsung|Apple|iPhone|Google|OnePlus|Xiaomi|Huawei|Sony|LG|Dell|HP|Lenovo|Microsoft|Nintendo)\b'
+        ]
+        for pattern in brand_patterns:
+            match = re.search(pattern, text, re.IGNORECASE)
+            if match:
+                result["brand"] = match.group(1)
+                break
+        
+        # Extract product name
+        product_patterns = [
+            r'(Galaxy\s+\w+(?:\s+\w+)?)',
+            r'(iPhone\s+\d+(?:\s+\w+)?)',
+            r'(Pixel\s+\d+(?:\s+\w+)?)',
+            r'(\w+\s+\d+(?:\s+\w+)?)'
+        ]
+        for pattern in product_patterns:
+            match = re.search(pattern, text)
+            if match:
+                result["product_name"] = match.group(1).strip()
+                break
+        
+        # Extract price
+        price_patterns = [
+            r'Price\s*:?\s*\$(\d+(?:,\d{3})*(?:\.\d{2})?)',
+            r'\$(\d+(?:,\d{3})*(?:\.\d{2})?)'
+        ]
+        for pattern in price_patterns:
+            match = re.search(pattern, text, re.IGNORECASE)
+            if match:
+                result["price"] = f"${match.group(1)}"
+                break
+        
+        # Extract quantities
+        quantity_matches = re.findall(r'(\d+(?:GB|TB|MB|RAM|Storage))', text, re.IGNORECASE)
+        result["quantities"] = [q for q in quantity_matches if q]
+        
+        # Try NER model too
+        if model_loaded:
+            ner_results = ner_pipeline(text)
+            ner_info = "\n🤖 **NER Model Results:**\n"
+            for entity in ner_results:
+                if entity.get('score', 0) > 0.3:
+                    ner_info += f"- {entity['word']} → {entity['entity_group']} ({entity['score']:.2f})\n"
+        else:
+            ner_info = "\n🤖 **NER Model:** Using fallback model\n"
+        
+        # Format output
+        output = f"""
+🏷️  **Brand:** {result['brand'] or 'Not found'}
+📱 **Product:** {result['product_name'] or 'Not found'}
+💰 **Price:** {result['price'] or 'Not found'}
+📊 **Quantities:** {', '.join(result['quantities']) or 'Not found'}
+
+{ner_info}
+"""
+        
+        return output
+        
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# Create interface
+demo = gr.Interface(
+    fn=extract_product_info,
+    inputs=gr.Textbox(
+        label="Product Description",
+        placeholder="Enter product description here...",
+        lines=3,
+        value="Samsung Galaxy S23 Ultra, 12GB RAM, 256GB Storage, Price: $1199."
+    ),
+    outputs=gr.Textbox(
+        label="Extracted Information", 
+        lines=12
+    ),
+    title="🛒 E-commerce Product Information Extractor",
+    description="Extract product names, brands, prices, and quantities from product descriptions using fine-tuned NER model.",
+    examples=[
+        ["Samsung Galaxy S23 Ultra, 12GB RAM, 256GB Storage, Price: $1199."],
+        ["iPhone 14 Pro Max 256GB $1299 Apple"],
+        ["Google Pixel 7 Pro 12GB RAM $899"],
+        ["Sony WH-1000XM4 wireless headphones $349"],
+        ["Dell XPS 13 laptop 16GB DDR4 $1299"]
+    ],
+    theme="default",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()