app.py

from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
import tensorflow as tf
import numpy as np
import shutil
import os
from huggingface_hub import InferenceClient
import json

# Initialize FastAPI app
app = FastAPI()

# Class labels
class_labels = {
    0: 'Baked Potato', 1: 'Burger', 2: 'Crispy Chicken', 3: 'Donut', 4: 'Fries',
    5: 'Hot Dog', 6: 'Jalapeno', 7: 'Kiwi', 8: 'Lemon', 9: 'Lettuce',
    10: 'Mango', 11: 'Onion', 12: 'Orange', 13: 'Pizza', 14: 'Taquito', 
    15: 'Apple', 16: 'Banana', 17: 'Beetroot', 18: 'Bell Pepper', 19: 'Bread',
    20: 'Cabbage', 21: 'Carrot', 22: 'Cauliflower', 23: 'Cheese',
    24: 'Chilli Pepper', 25: 'Corn', 26: 'Crab', 27: 'Cucumber',
    28: 'Eggplant', 29: 'Eggs', 30: 'Garlic', 31: 'Ginger', 32: 'Grapes',
    33: 'Milk', 34: 'Salmon', 35: 'Yogurt'
}

# Load the trained model
model = tf.keras.models.load_model("model_unfreezeNewCorrectpredict.keras")

# Image preprocessing function
def load_and_prep_image(file_path, img_shape=224):
    img = tf.io.read_file(file_path)
    img = tf.image.decode_image(img, channels=3)
    img = tf.image.resize(img, size=[img_shape, img_shape])
    img = tf.expand_dims(img, axis=0)
    return img

# Predict label function
def predict_label(model, image_path, class_names):
    img = load_and_prep_image(image_path, img_shape=224)
    pred = model.predict(img)
    pred_class_index = np.argmax(pred, axis=1)[0]
    pred_class_name = class_names[pred_class_index]
    return pred_class_name


@app.get("/")
def read_root():
    return {"message": "This is My Nutrionguid App"}

# API endpoint for prediction
@app.post("/predict")
async def predict_image(file: UploadFile = File(...)):
    try:
        # Save the uploaded file
        file_location = f"./temp_{file.filename}"
        with open(file_location, "wb") as f:
            shutil.copyfileobj(file.file, f)
        
        # Predict the label
        prediction = predict_label(model, file_location, class_labels)
        
        # Remove the temporary file
        os.remove(file_location)
        
        return {"predicted_label": prediction}
    except Exception as e:
        return JSONResponse(
            status_code=500,
            content={"error": f"An error occurred: {str(e)}"}
        )
@app.post("/predictNUT")
async def predict_image_and_nutrition(file: UploadFile = File(...)):
    try:
        # Save the uploaded file
        file_location = f"./temp_{file.filename}"
        with open(file_location, "wb") as f:
            shutil.copyfileobj(file.file, f)
        
        # Predict the label using the same prediction logic
        prediction = predict_label(model, file_location, class_labels)
        
        # Remove the temporary file
        os.remove(file_location)

        # Define the repository ID and your token
        #repo_id = "google/gemma-2-9b-it"
        repo_id = "Qwen/Qwen2.5-72B-Instruct"
        # repo_id = "microsoft/Phi-3-mini-4k-instruct"
        #repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
        api_token = "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21"

        # Initialize the InferenceClient with your token
        llm_client = InferenceClient(
            model=repo_id,
            token=api_token[:-2],  # Pass the token here
            timeout=120,
        )

        # Function to call the LLM
        def call_llm(inference_client: InferenceClient, prompt: str):
            response = inference_client.post(
                json={
                    "inputs": prompt,
                    "parameters": {"max_new_tokens": 500},
                    "task": "text-generation",
                },
            )
            return json.loads(response.decode())[0]["generated_text"]
        
        # Use the prediction to generate nutrition information
        # prompt = f"Nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, Pantothenic acid) for {prediction} in formatted list"
        # # prompt = f"Provide all the nutrition information for {prediction}, including Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, and Pantothenic acid. Please present the information in a clear, formatted list only, without additional explanations."
        # response = call_llm(llm_client, prompt)

        # return {"predicted_label": prediction, "nutrition_info": response}

        # nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, Pantothenic acid) for {prediction} per 100 grams in a formatted list only."
        nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6) for {prediction} per 100 grams, Output the information as a concise, formatted list without repetition."
        nutrition_info = call_llm(llm_client, nutrition_prompt)
        
        # # Second prompt: Health benefits and tips
        health_benefits_prompt = f"Provide the health benefits and considerations for {prediction}. Additionally, include practical tips for making {prediction} healthier. Keep the response focused on these two aspects only."
        # health_benefits_prompt = f"Provide detailed information about {prediction}, including its origin, common uses, cultural significance, and any interesting facts. Keep the response informative and well-structured."
        Information = call_llm(llm_client, health_benefits_prompt)
        
        recipes_prompt=f"Tell me about the two most famous recipes for {prediction}. Include the ingredients only."
        recipes_info=call_llm(llm_client, recipes_prompt)

        return {
            "Predicted_label": prediction, 
            "Nutrition_info": nutrition_info,
            "Information": Information,
            "Recipes":recipes_info
        }
    except Exception as e:
        return JSONResponse(
            status_code=500,
            content={"error": f"An error occurred: {str(e)}"}
        )




        #nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6) for {prediction} in a formatted list only."
        # nutrition_info = call_llm(llm_client, nutrition_prompt)

        # # Second prompt: Health benefits and tips
        # health_benefits_prompt = f"Provide the health benefits and considerations for {prediction} and give tips for making it healthier."
        # health_benefits_and_tips = call_llm(llm_client, health_benefits_prompt)

        # return {
        #     "predicted_label": prediction, 
        #     "nutrition_info": nutrition_info,
        #     "health_benefits_and_tips": health_benefits_and_tips
        # }