initial start

app.py

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+# app.py
+import gradio as gr
+from transformers import pipeline
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import spacy
+
+# Load the zero-shot classification model
+model_name = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli"
+classifier = pipeline("zero-shot-classification", model=model_name)
+
+# Load spaCy for brand name extraction
+nlp = spacy.load("en_core_web_sm")
+
+def extract_brand_names(text):
+    doc = nlp(text)
+    # Look for organization names and proper nouns that might be brands
+    potential_brands = []
+    for ent in doc.ents:
+        if ent.label_ in ["ORG", "PRODUCT"]:
+            potential_brands.append((ent.text, 0.9))  # High confidence for named entities
+    
+    # Also check for proper nouns that might be brands
+    for token in doc:
+        if token.pos_ == "PROPN" and token.text not in [brand[0] for brand in potential_brands]:
+            potential_brands.append((token.text, 0.7))  # Lower confidence for proper nouns
+    
+    return potential_brands
+
+def classify_product(ad_text):
+    if not ad_text.strip():
+        return "Please enter some ad text."
+    
+    # Category classification
+    category_hypothesis = "This is an advertisement for a product in the category of"
+    candidate_categories = [
+        "Software", "Electronics", "Clothing", "Food & Beverage", 
+        "Healthcare", "Financial Services", "Entertainment",
+        "Home & Garden", "Automotive", "Education"
+    ]
+    
+    category_result = classifier(
+        ad_text,
+        candidate_labels=candidate_categories,
+        hypothesis_template=category_hypothesis,
+        multi_label=False
+    )
+    
+    # Product type classification
+    product_hypothesis = "This is specifically a"
+    # We'll let the model infer specific product types based on the text
+    product_result = classifier(
+        ad_text,
+        candidate_labels=[
+            "software application", "mobile app", "subscription service",
+            "physical product", "digital product", "professional service",
+            "consumer device", "platform", "tool"
+        ],
+        hypothesis_template=product_hypothesis,
+        multi_label=False
+    )
+    
+    # Brand extraction
+    brands = extract_brand_names(ad_text)
+    
+    # Format results
+    results = {
+        "Category": {
+            "classification": category_result["labels"][0],
+            "confidence": f"{category_result['scores'][0]:.2%}"
+        },
+        "Product Type": {
+            "classification": product_result["labels"][0],
+            "confidence": f"{product_result['scores'][0]:.2%}"
+        },
+        "Detected Brands": [
+            {"brand": brand, "confidence": f"{conf:.2%}"}
+            for brand, conf in brands
+        ] if brands else "No specific brand detected"
+    }
+    
+    # Format output string
+    output = f"""
+📊 Analysis Results:
+
+🏷️ Category: {results['Category']['classification']}
+   Confidence: {results['Category']['confidence']}
+
+📦 Product Type: {results['Product Type']['classification']}
+   Confidence: {results['Product Type']['confidence']}
+
+🏢 Brand Detection:"""
+    
+    if isinstance(results["Detected Brands"], list):
+        for brand_info in results["Detected Brands"]:
+            output += f"\n   • {brand_info['brand']} (Confidence: {brand_info['confidence']})"
+    else:
+        output += f"\n   {results['Detected Brands']}"
+    
+    return output
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_product,
+    inputs=gr.Textbox(
+        lines=5,
+        placeholder="Paste your ad text here (max 100 words)...",
+        label="Advertisement Text"
+    ),
+    outputs=gr.Textbox(label="Analysis Results"),
+    title="AI Powered Product Identifier from Ad Text",
+    description="Paste your marketing ad text to identify the product category, type, and brand mentions.",
+    examples=[
+        ["Experience seamless productivity with our new CloudWork Pro subscription. This AI-powered workspace solution helps remote teams collaborate better with smart document sharing, real-time editing, and integrated chat features. Starting at $29/month."],
+        ["Introducing the new iPhone 15 Pro with revolutionary A17 Pro chip. Capture stunning photos with our advanced 48MP camera system. Available in titanium finish with all-day battery life. Pre-order now at Apple stores nationwide."],
+    ],
+    theme=gr.themes.Soft()
+)

requirements.txt

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+gradio==4.7.1
+transformers==4.34.0
+torch==2.0.1
+spacy==3.7.2
+https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.0/en_core_web_sm-3.7.0.tar.gz