Aplicativo detecção de objtetos e criação de receita com HF e OpenAI

recipe_app.py

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+"""
+Data Scientist.: Dr. Eddy Giusepe Chirinos Isidro
+
+Link de estudo:
+
+https://youtu.be/nGjHq7sXoSQ
+"""
+
+import tkinter as tk
+from tkinter import filedialog
+from PIL import Image, ImageTk
+import requests
+import os
+import openai
+import torch
+from transformers import DetrForObjectDetection, DetrImageProcessor
+
+# OpenAI API key
+openai.api_key = os.getenv("openai_key")
+
+# Initialize tkinter window
+root = tk.Tk()
+canvas = tk.Canvas(root, height=600, width=800)
+canvas.pack()
+
+# Create Detr object
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101")
+
+# Create a frame for the canvas
+frame = tk.Frame(root, bg='#80c1ff', bd=5)
+frame.place(relx=0.15, rely=0.15, relwidth=0.7, relheight=0.7)
+
+# Create an upload button
+uploadButton = tk.Button(root, text='Upload Image', command=lambda: upload())
+uploadButton.pack()
+
+# Create a recipe button
+recipeButton = tk.Button(root, text='Generate Recipe', command=lambda: generate_recipe())
+recipeButton.pack()
+
+# Create a quit button
+quitButton = tk.Button(root, text='Quit', command=root.quit)
+quitButton.pack()
+
+# Create a label for the recipe, wrap the text with 500 characters
+recipe = tk.Label(frame, text="Recipe", bg="lightblue", wraplength=500)
+recipe.pack()
+
+def upload():
+    # Get the filename from the dialog
+    filename = filedialog.askopenfilename(initialdir="/", title="Select an image", filetypes=(("JPG files", "*.jpg"), ("JPEG files", "*.jpeg"), ("PNG files", "*.png")))
+
+    # Load the image
+    global image
+    image = Image.open(filename)
+
+    # Resize the image
+    resized = image.resize((800, 600), Image.ANTIALIAS)
+
+    # Create a PhotoImage object
+    global photo
+    photo = ImageTk.PhotoImage(resized)
+
+    # Add the image to the canvas
+    canvas.create_image(0, 0, image=photo, anchor=tk.NW)
+
+
+def generate_recipe():
+    # Run the model
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+
+    # Convert outputs (bounding boxes and class logits) to COCO API
+    # Let's only keep detections with score > 0.9
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]
+
+    # Get the list of detected objects
+    detected_objects = []
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        detected_objects.append(model.config.id2label[label.item()])
+
+    # Generate the recipe
+    prompt = f"Create a recipe from the edible items from this list. Do not use anything that is not on the list: {detected_objects}"
+    response = openai.Completion.create(
+        model="text-davinci-003",
+        prompt=prompt,
+        temperature=0.7,
+        max_tokens=256,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0
+    )
+
+    # Update the recipe label
+    recipe.config(text=response["choices"][0]["text"])
+
+
+root.mainloop()