Create app/outfit.py

app/outfit.py

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+from fastapi import APIRouter
+from pydantic import BaseModel
+from typing import List
+import requests
+import json
+import os
+
+router = APIRouter(prefix="/outfit", tags=["Outfit"])
+
+WARDROBE_API_URL = "https://wardrobestudio.net/wardrobe/items"
+HF_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1"
+HF_TOKEN = os.getenv("HF_TOKEN")  # Set in Hugging Face Secrets
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+class Item(BaseModel):
+    id: str
+    label: str
+    image_url: str
+
+class OutfitSuggestion(BaseModel):
+    day: str
+    items: List[Item]
+
+def classify_with_clip(image_url: str) -> str:
+    return "jacket" if "jacket" in image_url.lower() else "clothing"
+
+def get_llm_recommendation(items: List[dict], weather_forecast: List[str]) -> List[dict]:
+    prompt = f"""
+You are a fashion stylist. Here is a user's wardrobe. Each item has a unique ID, label, and image:
+
+{json.dumps(items, indent=2)}
+
+7-day forecast: {', '.join(weather_forecast)}.
+
+Suggest 7 outfits (2–3 item ids per day) for the week. Respond as JSON:
+
+[
+  {{"day": "Monday", "items": ["item1", "item3"]}},
+  ...
+]
+""".strip()
+
+    response = requests.post(HF_API_URL, headers=HEADERS, json={"inputs": prompt})
+    response.raise_for_status()
+    result = response.json()
+
+    if isinstance(result, dict) and "error" in result:
+        raise RuntimeError(f"Hugging Face API error: {result['error']}")
+
+    generated_text = result[0].get("generated_text", "")
+    return json.loads(generated_text.split("```")[0].strip())
+
+@router.get("/weekly", response_model=List[OutfitSuggestion])
+async def generate_outfits():
+    try:
+        res = requests.get(WARDROBE_API_URL)
+        res.raise_for_status()
+        wardrobe = res.json()
+    except Exception as e:
+        return [{"day": "Error", "items": [{"id": "error", "label": "Wardrobe fetch failed", "image_url": ""}]}]
+
+    labeled_items = []
+    for idx, item in enumerate(wardrobe):
+        image_path = item.get("image_url")
+        image_url = f"https://wardrobestudio.net{image_path}"
+        label = classify_with_clip(image_url)
+        labeled_items.append({
+            "id": f"item{idx+1}",
+            "label": label,
+            "image_url": image_path
+        })
+
+    weather = ["sunny", "rainy", "cloudy", "cold", "warm", "hot", "windy"]
+
+    try:
+        outfits_raw = get_llm_recommendation(labeled_items, weather)
+        result = []
+        for entry in outfits_raw:
+            matched_items = [item for item in labeled_items if item["id"] in entry.get("items", [])]
+            result.append({
+                "day": entry.get("day", "Unknown"),
+                "items": matched_items
+            })
+        return result
+    except Exception as e:
+        return [{"day": "Error", "items": [{"id": "error", "label": f"LLM failed: {e}", "image_url": ""}]}]