Upload community_contributions/ederhion_week8/agents/items.py with huggingface_hub

community_contributions/ederhion_week8/agents/items.py

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+from pydantic import BaseModel
+from datasets import load_dataset
+from typing import Optional, List
+
+PREFIX = "Price is $"
+QUESTION = "What is the fair market value of this used car to the nearest dollar?"
+
+class Item(BaseModel):
+    """
+    An Item is a data-point representing a used car with a price.
+    """
+    title: str
+    category: str
+    price: float
+    full: Optional[str] = None
+    weight: Optional[float] = None
+    summary: Optional[str] = None
+    prompt: Optional[str] = None
+    id: Optional[int] = None
+
+    def make_prompt(self, text: str):
+        self.prompt = f"{QUESTION}\n\n{text}\n\n{PREFIX}{round(self.price)}.00"
+
+    def test_prompt(self) -> str:
+        return self.prompt.split(PREFIX)[0] + PREFIX
+
+    def __repr__(self) -> str:
+        return f"<{self.title} = ${self.price}>"
+
+    @classmethod
+    def from_hub(cls, dataset_name: str) -> tuple[List['Item'], List['Item'], List['Item']]:
+        """
+        Load a used car dataset from Hugging Face (HF) and map it to our Item schema.
+        We use 'Carson-Shively/used-car-price' as it is a standard automotive dataset.
+        """
+        ds = load_dataset(dataset_name, split="train")
+        ds = ds.shuffle(seed=42)
+        
+        items = []
+        for row in ds:
+            make = row.get("Make", "Unknown Make")
+            model = row.get("Model", "Unknown Model")
+            year = row.get("Year", "Unknown Year")
+            mileage = row.get("Mileage", "Unknown Mileage")
+            price = float(row.get("Price", 0.0))
+            
+            if price <= 0:
+                continue
+                
+            summary = f"{year} {make} {model} with {mileage} miles. Good condition used car."
+            
+            item = cls(
+                title=f"{year} {make} {model}",
+                category="Automotive",
+                price=price,
+                summary=summary
+            )
+            items.append(item)
+            
+            # Capping at 10,000 for high-speed local processing
+            if len(items) >= 10000:
+                break
+                
+        train_size = int(len(items) * 0.8)
+        val_size = int(len(items) * 0.1)
+        
+        train = items[:train_size]
+        val = items[train_size:train_size + val_size]
+        test = items[train_size + val_size:]
+        
+        return train, val, test