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
from langchain_community.agent_toolkits import GmailToolkit
import time
import requests
from datetime import datetime


# Initialize FastAPI app
app = FastAPI()

@app.get("/")
def read_root():
    # toolkit = GmailToolkit()
    return {"message": "Connection"}
    keep_alive()


@app.get("/ping")
def ping():
    return {"status": "alive"}
    
UPLOAD_FOLDER = "uploaded_pdfs"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)

@app.post("/upload-pdf")
async def upload_pdf(file: UploadFile = File(...)):
    try:
        file_location = os.path.join(UPLOAD_FOLDER, file.filename)
        with open(file_location, "wb") as buffer:
            shutil.copyfileobj(file.file, buffer)
        return JSONResponse(content={"message": f"Uploaded {file.filename} successfully"}, status_code=200)
    except Exception as e:
        return JSONResponse(content={"error": str(e)}, status_code=500)


@app.get("/list-files")
def list_uploaded_files():
    files = os.listdir(UPLOAD_FOLDER)
    return {"files": files}



def keep_alive(space_url="https://1mr-apigmail.hf.space/ping", interval_hours=5):
    while True:
        try:
            print(f"🔄 Pinging {space_url} at {datetime.now()}")
            response = requests.get(space_url)

            if response.status_code == 200:
                print("")
            else:
                print("")
        except Exception as e:
            print("")
        
        time.sleep(interval_hours * 3600)

# Call the function
#
        keep_alive()



# # 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
#         # }