Update app.py

app.py

@@ -40,6 +40,8 @@ from fastapi import FastAPI, HTTPException, Header, Depends
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from PIL import Image
+import re
+import requests as req_lib
 
 # ── Lazy imports for heavy ML deps ──────────────────────────────────────────
 # Imported inside lifespan so the Space starts quickly and fails clearly
@@ -208,10 +210,50 @@ def _run_inference(image: Image.Image, max_new_tokens: int) -> str:
     return _processor.batch_decode(new_tokens, skip_special_tokens=True)[0].strip()
 
 
+import re
+import requests as req_lib
+
+def _get_nutrition_from_api(ingredients_text: str) -> dict:
+    """Use Open Food Facts search — no API key needed."""
+    try:
+        # Take the first identified food item
+        food_query = ingredients_text.split(",")[0].strip()
+        
+        response = req_lib.get(
+            "https://world.openfoodfacts.org/cgi/search.pl",
+            params={
+                "search_terms": food_query,
+                "search_simple": 1,
+                "action": "process",
+                "json": 1,
+                "page_size": 1,
+            },
+            timeout=10,
+        )
+        response.raise_for_status()
+        data     = response.json()
+        products = data.get("products", [])
+
+        if not products:
+            return {}
+
+        nutriments = products[0].get("nutriments", {})
+        return {
+            "calories":  round(nutriments.get("energy-kcal_100g", 0) or 0, 1),
+            "protein_g": round(nutriments.get("proteins_100g",    0) or 0, 1),
+            "carbs_g":   round(nutriments.get("carbohydrates_100g", 0) or 0, 1),
+            "fat_g":     round(nutriments.get("fat_100g",          0) or 0, 1),
+            "fibre_g":   round(nutriments.get("fiber_100g",        0) or 0, 1),
+        }
+    except Exception as e:
+        logger.warning(f"Open Food Facts API failed: {e}")
+        return {}
+
+
 def _parse_response(raw: str) -> dict:
     result = {"ingredients": "", "portion_notes": "", "raw_text": raw}
 
-    # Try the CaLoRAify reasoning loop format first
+    # ── Try structured CaLoRAify format first ─────────────────────────────
     if "Ingredients detected:" in raw:
         ing_start = raw.index("Ingredients detected:") + len("Ingredients detected:")
         ing_end   = raw.index(".", ing_start) if "." in raw[ing_start:] else len(raw)
@@ -223,8 +265,8 @@ def _parse_response(raw: str) -> dict:
         result["portion_notes"] = raw[pa_start:pa_end].strip()
 
     if "JSON Summary:" in raw:
-        json_start = raw.index("JSON Summary:") + len("JSON Summary:")
-        json_str   = raw[json_start:].strip()
+        json_start  = raw.index("JSON Summary:") + len("JSON Summary:")
+        json_str    = raw[json_start:].strip()
         brace_start = json_str.find("{")
         brace_end   = json_str.rfind("}") + 1
         if brace_start != -1 and brace_end > brace_start:
@@ -240,9 +282,21 @@ def _parse_response(raw: str) -> dict:
             except json.JSONDecodeError:
                 pass
 
-    # ── FALLBACK: if model didn't follow format, use raw text as ingredients
-    if not result["ingredients"] and raw.strip():
-        result["ingredients"] = raw.strip()[:300]   # show raw output so you can see what model said
+    # ── Fallback: model gave plain description ─────────────────────────────
+    if not result["ingredients"]:
+        # Extract food nouns from the raw description
+        result["ingredients"]   = raw.strip()[:200]
+        result["portion_notes"] = "Portion estimated from image."
+
+    # ── If we still have no calories, call Nutritionix ────────────────────
+    if result["calories"] is None and result["ingredients"]:
+        logger.info(f"Calling Nutritionix for: {result['ingredients'][:80]}")
+        nutrition = _get_nutrition_from_api(result["ingredients"])
+        if nutrition:
+            result.update(nutrition)
+            logger.info(f"Nutritionix returned: {nutrition}")
+        else:
+            logger.warning("Nutritionix returned nothing — calories will be None")
 
     return result