File size: 4,916 Bytes
74d6c39 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | import os
from flask import Flask, request, jsonify, render_template
import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
from openai import OpenAI
from flask_cors import CORS
from fridgeItemDetector import fridge_item_detector, black_list_classes
app = Flask(__name__, static_url_path='/static')
CORS(app) # 允许所有来源的跨域请求
# 初始化 Flask 应用
app = Flask(__name__)
# # 加载预训练的 ResNet50 模型
# model = models.resnet50(pretrained=True)
# # 重新定义最后的全连接层，适应 67 个类别
# num_features = model.fc.in_features
# model.fc = nn.Linear(num_features, 67)
# # 加载模型权重，但忽略 fc 层
# state_dict = torch.load('resnet50_food_classifier.pth', map_location=torch.device('cpu'))
# # 删除 state_dict 中全连接层的权重
# del state_dict['fc.weight']
# del state_dict['fc.bias']
# # 加载剩余的权重
# model.load_state_dict(state_dict, strict=False)
# model.eval()
# 定义数据预处理
# transform = transforms.Compose([
# transforms.Resize((224, 224)),
# transforms.ToTensor(),
# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
# ])
open_ai_api_key = os.getenv("OPEN_AI_KEY")
# 设置 LLM
client = OpenAI(api_key=open_ai_api_key)
def generate_recipe(ingredients_text):
prompt = f"Ingredients: {ingredients_text}. List 3 possible recipes with these ingredients and provide the essential cooking steps only."
message = [{"role": "system", "content": prompt}]
try:
completion = client.chat.completions.create(
model="gpt-4o-mini",
messages=message,
temperature=0.38,
max_tokens=500
)
# 返回的菜谱内容中，每个步骤用换行符分隔
return completion.choices[0].message.content
except Exception as e:
return f"Error generating recipe: {str(e)}"
# 路由：主页
@app.route('/')
def home():
return render_template('index.html') # 前端页面
# 路由：上传图片并识别
@app.route('/upload', methods=['POST'])
def upload_image():
try:
# 检查是否有文件上传
if 'fridge-image' not in request.files:
return jsonify({"error": "No file part"})
file = request.files['fridge-image']
if file.filename == '':
return jsonify({"error": "No selected file"})
# try:
# # 尝试打开并转换图片
# img = Image.open(file).convert('RGB')
# img = transform(img).unsqueeze(0) # 增加 batch 维度
# except Exception as e:
# return jsonify({"error": f"Error processing image: {str(e)}"})
try:
# 进行模型推理
result = fridge_item_detector.detect_objects(
file,
prob_cutoff=0.9,
black_list_classes=black_list_classes,
annotate_image=False,
return_unique_label=True
)
detected_ingredient = ", ".join(result)
except Exception as e:
return jsonify({"error": f"Error during model inference: {str(e)}"})
# 根据食材生成菜谱
try:
# ingredients = [ "apple", "asparagus", "aubergine", "bacon", "banana", "basil", "beans",
# "beef", "beetroot", "bell pepper", "bitter gourd", "blueberries", "broccoli",
# "cabbage", "carrot", "cauliflower", "cheese", "chicken", "chillies",
# "chocolate", "coriander", "corn", "courgettes", "cream", "cucumber", "dates",
# "egg", "flour", "garlic", "ginger", "green beans", "green chilies", "ham",
# "juice", "lemon", "lettuce", "lime", "mango", "meat", "mineral water",
# "mushroom", "olive", "onion", "orange", "parsley", "peach", "peas", "peppers",
# "potato", "pumpkin", "red grapes", "red onion", "salami", "sauce", "sausage",
# "shallot", "shrimp", "spinach", "spring onion", "strawberry", "sugar",
# "sweet potato", "swiss butter", "swiss jam", "swiss yoghurt", "tomato", "watermelon"] # 所有的食材
# detected_ingredient = ingredients[predicted_class]
recipe = generate_recipe(detected_ingredient)
except Exception as e:
return jsonify({"error": f"Error generating recipe: {str(e)}"})
return jsonify({"ingredient": detected_ingredient, "recipe": recipe})
except Exception as e:
return jsonify({"error": f"Unexpected error: {str(e)}"})
if __name__ == '__main__':
app.run(debug=True)
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