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app.py

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+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()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:41e869f27c180a813c57ad97b93864c801a321df9a5b47434e4d64f59ece46b4
+size 6318051

requirements.txt

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+torch
+opencv-python
+numpy
+Pillow
+google-generativeai
+gradio
+ultralytics