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from fastapi import FastAPI, File, UploadFile
import google.generativeai as genai
from PIL import Image
import io
import json
from google.colab import userdata
app = FastAPI()
# Set your API key
genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))
@app.get("/")
def read_root():
return {"message": "FastAPI is running on Render!"}
@app.head("/")
async def root_head():
return {} # Empty response for HEAD requests
@app.post("/analyze")
async def analyze_food_image(file: UploadFile = File(...)):
"""Analyzes food items in the image using Gemini 1.5 Flash"""
image_bytes = await file.read()
img = Image.open(io.BytesIO(image_bytes))
model = genai.GenerativeModel("gemini-1.5-flash") # Best for real-time image processing
# Updated prompt for structured JSON response
prompt = """
Identify all food items in the given image and determine their approximate quantity. Then, provide nutritional information
in valid JSON format following this structure:
```json
{
"detected_food_items": [
{
"food_item": "Detected food name",
"quantity": "Approximate quantity (e.g., 1 bowl, 2 slices, half a chapati)",
"nutritional_info": {
"calories_kcal": value,
"protein_g": value,
"fiber_g": value,
"vitamins": {
"Vitamin A_mcg": value,
"Vitamin C_mg": value,
"Vitamin D_mcg": value,
"Vitamin E_mg": value,
"Vitamin K_mcg": value,
"Vitamin B1_mg": value,
"Vitamin B2_mg": value,
"Vitamin B3_mg": value,
"Vitamin B6_mg": value,
"Vitamin B12_mcg": value,
"Folate_mcg": value
}
},
"health_benefits": [
"Brief description of health benefit 1",
"Brief description of health benefit 2"
]
}
]
}
```
- **Ensure the response is valid JSON** inside triple backticks (```json ... ```).
- **Include accurate nutritional values based on the given quantity.**
- **If exact values are unavailable, provide estimated values.**
- **Ensure proper formatting and completeness of data.**
"""
# Get response from API
response = model.generate_content([prompt, img])
# Extract JSON response
try:
json_response = response.text.strip("```json").strip("```") # Remove markdown formatting
return json.loads(json_response) # Convert to Python dictionary
except json.JSONDecodeError:
return {"error": "Invalid response format from AI model"}
import os
if __name__ == "__main__":
port = int(os.getenv("PORT", 8000)) # Render assigns PORT dynamically
uvicorn.run(app, host="0.0.0.0", port=port) |