Create demo version

demo version

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+# app.py - Gradio demo for testing your image-classification model (Food-101)
+# - Replace FOOD101_MODEL_ID with your model repo id (e.g., "yourname/your-food-model")
+# - If your model is private, add HF_TOKEN as a secret in your Space (or set env var)
+
+import os
+from PIL import Image
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import gradio as gr
+
+# ---- CONFIG: 改成你的 model id ----
+Tell_Me_Recipe = os.environ.get("Tell_Me_Recipe", "YOUR_USERNAME/YOUR_FOOD101_MODEL")
+# optional gate model (food / not-food). Change or set to None to skip.
+GATE_MODEL_ID = os.environ.get("GATE_MODEL_ID", "prithivMLmods/Food-or-Not-SigLIP2")
+HF_TOKEN = os.environ.get("HF_TOKEN")  # 用於 private 模型
+
+# ---- device ----
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ---- load models (一次載入) ----
+def load_model(model_id):
+    if model_id is None or model_id.strip() == "":
+        return None, None
+    try:
+        processor = AutoImageProcessor.from_pretrained(model_id, use_auth_token=HF_TOKEN)
+        model = AutoModelForImageClassification.from_pretrained(model_id, use_auth_token=HF_TOKEN)
+        model.to(device)
+        model.eval()
+        return processor, model
+    except Exception as e:
+        print(f"Failed to load {model_id}: {e}")
+        raise
+
+print("Loading main classification model:", Tell_Me_Recipe)
+clf_processor, clf_model = load_model(Tell_Me_Recipe)
+
+if GATE_MODEL_ID:
+    try:
+        print("Loading gate model (food vs not-food):", GATE_MODEL_ID)
+        gate_processor, gate_model = load_model(GATE_MODEL_ID)
+    except Exception as e:
+        print("Gate model load failed — continuing without gate:", e)
+        gate_processor, gate_model = None, None
+else:
+    gate_processor, gate_model = None, None
+
+# ---- small recipe template mapping (示範用) ----
+RECIPES = {
+    "pizza": {
+        "ingredients": ["高筋麵粉 300g", "水 180ml", "酵母 3g", "番茄醬", "Mozzarella 起司"],
+        "steps": ["揉麵發酵", "桿皮抹醬加起司配料", "220°C 烤 10-15 分鐘"]
+    },
+    "ramen": {
+        "ingredients": ["中華麵", "高湯 400ml", "醬油", "叉燒", "溏心蛋"],
+        "steps": ["煮麵 / 熬高湯 / 擺盤"]
+    },
+    "cheesecake": {
+        "ingredients": ["奶油乳酪 200g", "蛋 2 顆", "砂糖 60g", "消化餅底"],
+        "steps": ["餅乾壓底 / 乳酪餡打勻倒入 / 160°C 烤 40-50 分鐘"]
+    }
+}
+
+def simple_recipe_for(label: str):
+    key = label.replace("_", " ").lower()
+    for k in RECIPES:
+        if k in key:
+            r = RECIPES[k]
+            ing = "\n".join(f"- {i}" for i in r["ingredients"])
+            steps = "\n".join(f"{idx+1}. {s}" for idx, s in enumerate(r["steps"]))
+            return f"【材料】\n{ing}\n\n【步驟】\n{steps}"
+    return f"找不到精確食譜。模型預測：{label.replace('_',' ')}。\n建議搜尋該菜名的食譜或連到 RecipeNLG 做檢索。"
+
+# ---- inference helpers ----
+@torch.inference_mode()
+def is_food_image(image: Image.Image, threshold: float = 0.5):
+    """如果有 gate_model，回傳 (is_food:bool, score:float, label:str)"""
+    if gate_model is None or gate_processor is None:
+        return True, 1.0, "no-gate"
+    inputs = gate_processor(images=image, return_tensors="pt")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    out = gate_model(**inputs)
+    probs = out.logits.softmax(-1).squeeze(0)
+    topv, topi = torch.max(probs, dim=-1)
+    label = gate_model.config.id2label[int(topi)]
+    # 假設 gate 的 label 包含 'not' 或 'not_food' 來表現非食物
+    not_food_names = ["not-food", "not_food", "not food", "notfood", "no-food"]
+    is_food = True
+    if any(n in label.lower() for n in not_food_names):
+        is_food = False
+    return is_food, float(topv), label
+
+@torch.inference_mode()
+def predict_label(image: Image.Image, topk: int = 3):
+    inputs = clf_processor(images=image, return_tensors="pt")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    out = clf_model(**inputs)
+    probs = out.logits.softmax(-1).squeeze(0)
+    topv, topi = torch.topk(probs, k=min(topk, probs.shape[0]))
+    labels = [clf_model.config.id2label[int(i)] for i in topi]
+    return [(l.replace("_"," "), float(v)) for l, v in zip(labels, topv)]
+
+# ---- gradio UI function ----
+def analyze_image(image: Image.Image, topk: int=3, gate_threshold: float=0.5, use_gate: bool=True):
+    if image is None:
+        return "請上傳圖片", "", "", ""
+    try:
+        # 1) gate (optional)
+        if use_gate and gate_model is not None:
+            is_food, score, gate_label = is_food_image(image, gate_threshold)
+            if not is_food and score >= gate_threshold:
+                return f"判斷：非食物（{gate_label}，score={score:.2f}）", "", "", "這張圖被判定為「非食物」，不做菜名預測。"
+        # 2) predict top-k
+        preds = predict_label(image, topk=topk)
+        topk_text = "\n".join([f"{lbl}：{p:.3f}" for lbl,p in preds])
+        best_label = preds[0][0]
+        recipe_txt = simple_recipe_for(best_label)
+        return f"判斷：是食物（gate ok）", best_label, topk_text, recipe_txt
+    except Exception as e:
+        return f"發生錯誤：{e}", "", "", ""
+
+# ---- build UI ----
+with gr.Blocks() as demo:
+    gr.Markdown("# Food → 菜名 + 簡易食譜 Demo")
+    with gr.Row():
+        img = gr.Image(type="pil", label="上傳一張照片（任意圖片）")
+        with gr.Column():
+            topk = gr.Slider(1, 10, value=3, step=1, label="Top-K")
+            gate_check = gr.Checkbox(value=True, label="使用 food vs not-food gate（建議開）")
+            gate_th = gr.Slider(0.0, 1.0, value=0.5, label="Gate threshold")
+            run_btn = gr.Button("分析照片")
+    with gr.Row():
+        out0 = gr.Textbox(label="是否為食物 / Gate 訊息", lines=1)
+    with gr.Row():
+        out1 = gr.Textbox(label="預測菜名（Top 1）", lines=1)
+    with gr.Row():
+        out2 = gr.Textbox(label="Top-K 預測 & 機率", lines=6)
+    with gr.Row():
+        out3 = gr.Textbox(label="自動回傳的簡易食譜（示範）", lines=12)
+    run_btn.click(fn=analyze_image, inputs=[img, topk, gate_th, gate_check], outputs=[out0, out1, out2, out3])
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))