Create predict.py

predict.py

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+import gradio as gr
+import json
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from sklearn.neighbors import NearestNeighbors
+
+# Load model and data
+model = SentenceTransformer('models/ad_categorizer')
+with open('data/listings.json') as f:
+    listings = json.load(f)
+
+# Prepare embeddings
+texts = [item['text'] for item in listings]
+embeddings = model.encode(texts)
+categories = [item['category'] for item in listings]
+
+# Create search index
+nn = NearestNeighbors(n_neighbors=1).fit(embeddings)
+
+def categorize(text):
+    # Encode query
+    query_embedding = model.encode(text)
+    
+    # Find nearest match
+    _, indices = nn.kneighbors([query_embedding])
+    best_match = listings[indices[0][0]]
+    
+    return {
+        "category": best_match['category'],
+        "category_id": best_match['category_id'],
+        "similar_listing": best_match['text']
+    }
+
+# Gradio interface
+demo = gr.Interface(
+    fn=categorize,
+    inputs=gr.Textbox(label="Ad Listing"),
+    outputs=gr.JSON(label="Prediction"),
+    examples=json.load(open('data/test_cases.json'))
+)
+
+demo.launch()