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53767de | 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 | import torch
import cv2
import numpy as np
from PIL import Image
import google.generativeai as genai
import gradio as gr
from ultralytics import YOLO
import os
# Force PyTorch to use CPU
device = torch.device("cpu")
# Configure Google AI API
# genai.configure(api_key='AIzaSyATgL92t9qBCe4eqFX1cSfyfkzKMooQM48')
# model = genai.GenerativeModel('gemini-2.0-flash')
genai.configure(api_key='AIzaSyATgL92t9qBCe4eqFX1cSfyfkzKMooQM48')
generation_config = {
"temperature": 1,
"top_p": 0.95,
"top_k": 40,
"max_output_tokens": 8192,
"response_mime_type": "text/plain",
}
model = genai.GenerativeModel(
model_name="gemini-1.5-flash",
generation_config=generation_config,
)
# Test Gemini API connection
try:
test_response = model.generate_content("Say Hello!")
print("Test Response from Gemini:", test_response.text)
except Exception as e:
print("Error: Gemini API connection failed! Check API key.")
print(e)
exit()
class FoodDetectionSystem:
def __init__(self, model_path):
self.model = YOLO('best.pt') # Load trained YOLOv8 model
self.model.to(device) # Ensure model runs on CPU
def detect_food(self, image_path):
"""Detect food items in the image"""
results = self.model(image_path) # Run inference on image
detected_items = []
for result in results:
boxes = result.boxes.cpu() # Ensure bounding boxes are on CPU
for box in boxes:
class_id = int(box.cls[0])
conf = float(box.conf[0])
if conf > 0.5: # Confidence threshold
detected_items.append(result.names[class_id])
return list(set(detected_items)) # Remove duplicates
def generate_recipe(ingredients, calorie_requirement):
"""Generate recipe using Gemini AI"""
prompt = f"""
Create a healthy recipe using some or all of these ingredients: {', '.join(ingredients)}.
The recipe should be approximately {calorie_requirement} calories suggest 2 recipies.
Please provide:
Recipi Number
1. Recipe name
2. Ingredients list with quantities
3. Step-by-step instructions
4. Approximate calorie count per serving
"""
response = model.generate_content(prompt)
return response.text
def process_image_and_generate_recipe(image, calorie_requirement):
"""Main function to process image and generate recipe"""
try:
# Save uploaded image temporarily
temp_path = "temp_upload.jpg"
image.save(temp_path)
print("Image saved at:", temp_path)
print("food detection start")
# Initialize and use food detection system
detector = FoodDetectionSystem('path_to_your_trained_model.pt')
detected_foods = detector.detect_food(temp_path)
print(detected_foods)
if not detected_foods:
return "No food items detected in the image. Please try another image."
# Generate recipe
recipe = generate_recipe(detected_foods, calorie_requirement)
# Clean up
os.remove(temp_path)
return f"Detected Foods: {', '.join(detected_foods)}\n\nGenerated Recipe:\n{recipe}"
except Exception as e:
return f"An error occurred: {str(e)}"
# Create Gradio interface
def create_interface():
iface = gr.Interface(
fn=process_image_and_generate_recipe,
inputs=[
gr.Image(type="pil", label="Upload Food Image"),
gr.Number(label="Desired Calorie Count", value=500)
],
outputs=gr.Textbox(label="Results"),
title="Food Detection and Recipe Generator",
description="Upload a food image to detect ingredients and generate a recipe based on your calorie requirements."
)
return iface
if __name__ == "__main__":
iface = create_interface()
iface.launch() |