Update app.py

app.py

@@ -1 +1,100 @@
-python <br># ------------------------------------------------------------ <br># FastAPI service exposing BinhQuocNguyen/food‑recognition‑model <br># ------------------------------------------------------------ <br>from fastapi import FastAPI, HTTPException <br>from pydantic import BaseModel <br>import base64, io <br>from PIL import Image <br>from transformers import pipeline <br> <br># ------------------------------------------------------------------- <br># 1️⃣ Load the model once at startup (fast, no re‑load per request) <br># ------------------------------------------------------------ <br>classifier = pipeline( <br> \"image-classification\", <br> model=\"BinhQuocNguyen/food-recognition-model\" <br>) <br> <br># ------------------------------------------------------------------- <br># 2️⃣ Simple in‑memory “nutritional DB” – you can extend it with all 101 classes <br># ------------------------------------------------------------------- <br># (calories per 100 g, avg portion weight in grams) <br>nutrient_db = { <br> \"Apple\": {\"calories_per_100g\": 52, \"portion_g\": 182}, <br> \"Banana\": {\"calories_per_100g\": 89, \"portion_g\": 118}, <br> \"Orange\": {\"calories_per_100g\": 43, \"portion_g\": 131}, <br> \"Pizza\": {\"calories_per_100g\": 266, \"portion_g\": 200}, <br> \"Bread\": {\"calories_per_100g\": 265, \"portion_g\": 30}, <br> # ← add the rest of your favourite foods here <br>} <br> <br># ------------------------------------------------------------------- <br># 3️⃣ Pydantic model for the incoming JSON payload <br># ------------------------------------------------------------------- <br>class ImageRequest(BaseModel): <br> image: str <br> <br>app = FastAPI() <br> <br># ------------------------------------------------------------ <br># Health‑check endpoint (optional) <br># ------------------------------------------------------------ <br>@app.get(\"/\") <br>def root(): <br> return {\"message\": \"Food‑Recognition API is up\"} <br> <br># ------------------------------------------------------------ <br># 4️⃣ Main inference endpoint <br># ------------------------------------------------------------ <br>@app.post(\"/analyze\") <br>def analyze(request: ImageRequest): <br> # ---- decode the base64 image -------------------------------- <br> try: <br> raw_bytes = base64.b64decode(request.image) <br> pil_img = Image.open(io.BytesIO(raw_bytes)).convert(\"RGB\") <br> except Exception as e: <br> raise HTTPException(status_code=400, detail=\"Invalid base64 image\") <br> <br> # ---- run the classifier -------------------------------------- <br> results = classifier(pil_img) <br> if not results: <br> raise HTTPException(status_code=500, detail=\"Model returned no results\") <br> top = results[0] <br> label = top.get(\"label\") <br> confidence = top.get(\"score\") <br> <br> # ---- look up nutrition data ----------------------------------- <br> # The model’s own DB is not directly exposed, so we use the inline dict above <br> nutrition = nutrient_db.get(label, {\"calories_per_100g\": 0, \"portion_g\": 100}) <br> calories_per_100g = nutrition[\"calories_per_100g\"] <br> portion_g = nutrition[\"portion_g\"] <br> # Estimated calories for the (default) portion size <br> est_calories = calories_per_100g * (portion_g / 100.0) <br> <br> # ---- build the JSON response -------------------------------- <br> return { <br> \"label\": label, <br> \"confidence\": confidence, <br> \"estimated_portion_g\": portion_g, <br> \"calories_per_100g\": calories_per_100g, <br> \"estimated_calories\": round(est_calories, 2) <br> } <br>
+"""
+FastAPI service exposing BinhQuocNguyen/food‑recognition‑model.
+"""
+
+# ------------------------------------------------------------
+# 1️⃣ Imports
+# ------------------------------------------------------------
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import base64
+import io
+
+from PIL import Image
+from transformers import pipeline
+
+# ------------------------------------------------------------
+# 2️⃣ Load the model once at startup (fast, no re‑load per request)
+# ------------------------------------------------------------
+classifier = pipeline(
+    "image-classification",
+    model="BinhQuocNguyen/food-recognition-model"
+)
+
+# ------------------------------------------------------------
+# 3️⃣ Simple in‑memory “nutritional DB”
+#    (calories per 100 g, average portion weight in grams)
+# ------------------------------------------------------------
+nutrient_db = {
+    "Apple":  {"calories_per_100g": 52,  "portion_g": 182},
+    "Banana": {"calories_per_100g": 89,  "portion_g": 118},
+    "Orange": {"calories_per_100g": 43,  "portion_g": 131},
+    "Pizza":  {"calories_per_100g": 266, "portion_g": 200},
+    "Bread":  {"calories_per_100g": 265, "portion_g": 30},
+    # ← add the rest of your favourite foods here
+}
+
+# ------------------------------------------------------------
+# 4️⃣ Pydantic model for the incoming JSON payload
+# ------------------------------------------------------------
+class ImageRequest(BaseModel):
+    """Base64‑encoded image sent by the client."""
+    image: str
+
+
+# ------------------------------------------------------------
+# 5️⃣ FastAPI app & health‑check endpoint
+# ------------------------------------------------------------
+app = FastAPI()
+
+
+@app.get("/")
+def root():
+    """Simple health‑check."""
+    return {"message": "Food‑Recognition API is up"}
+
+
+# ------------------------------------------------------------
+# 6️⃣ Main inference endpoint
+# ------------------------------------------------------------
+@app.post("/analyze")
+def analyze(request: ImageRequest):
+    """
+    1️⃣ Decode the base64 image.
+    2️⃣ Run the classifier.
+    3️⃣ Look up (or fall back to) nutritional information.
+    4️⃣ Return a JSON response.
+    """
+    # ---- 1️⃣ decode the base64 image ---------------------------------
+    try:
+        raw_bytes = base64.b64decode(request.image)
+        pil_img = Image.open(io.BytesIO(raw_bytes)).convert("RGB")
+    except Exception as e:  # pragma: no cover
+        raise HTTPException(status_code=400, detail="Invalid base64 image") from e
+
+    # ---- 2️⃣ run the classifier --------------------------------------
+    results = classifier(pil_img)
+    if not results:
+        raise HTTPException(status_code=500, detail="Model returned no results")
+
+    top = results[0]
+    label = top.get("label")
+    confidence = top.get("score")
+
+    # ---- 3️⃣ look up nutrition data ----------------------------------
+    # Fallback values if the label isn’t in the DB
+    nutrition = nutrient_db.get(label, {"calories_per_100g": 0, "portion_g": 100})
+    calories_per_100g = nutrition["calories_per_100g"]
+    portion_g = nutrition["portion_g"]
+
+    # ---- 4️⃣ estimated calories for the default portion size ----------
+    est_calories = calories_per_100g * (portion_g / 100.0)
+
+    # ---- 5️⃣ build the JSON response --------------------------------
+    return {
+        "label": label,
+        "confidence": confidence,
+        "estimated_portion_g": portion_g,
+        "calories_per_100g": calories_per_100g,
+        "estimated_calories": round(est_calories, 2),
+    }