import requests
from dotenv import load_dotenv
import os

load_dotenv()

huggingface = os.getenv("HUGGINGFACE")


class TopicGenerator:

    def __init__(self):
        # Initialize API-URL and authorization headers
        self.url = "https://api-inference.huggingface.co/models/google/flan-t5-large"
        self.headers = {"Authorization": f"Bearer {huggingface}"}

    def query(self, payload):
        response = requests.post(self.url, headers=self.headers,
                                 json=payload)
        return response
    
    def generate_topics(self, user_input, num_topics=3):
        payload = {
            "inputs": f"""Generate a topic sentence idea based on the user input. 
            The generated topics should portray the context or idea behind the given sentences or phrase.
            For Instance,
                - "Grocery Shopping" OR "Grocery List" OR "Shopping List": "I'm going grocery shopping tomorrow, 
                and I would like to get the following things on my grocery list: Milk, Soybeans, Cowpeas, 
                Saturated Water, Onions, Tomatoes, etc."
                - "Studying For Exams" OR "Exams Studies": "Exams aare coming up and I have to prepare for the core 
                courses. I'll be studying for Control Systems, Software Engineering and Circuit Theory."
                - "Healthy Breakfast": "To prepare a healthy breakfast, I need the appropriate combination of balanced 
                diet. I'll need oats, yogurt, fresh berries, honey and smoothies."
                -  "Fitness Routine": "Starting a fitness routine involves workout clothes, running shoes, 
                a water bottles, and a gym membership. With this, I can start a proper fitness plan."
                - "Summer Vacation": "Packing swimsuits and enjoy the view of the ocean."
                - "Coffee Break": "Sipping Coffee at the table."
                - "Relaxation": "Sitting at the table enjoying."
                
            This is what I'm expecting the model to do. Here is the input: {user_input}
                       """,
            "do_sample": True,
            "temperature": 0.7,
            "num_return_sequences": num_topics
            }
        output = self.query(payload)
        if output.status_code == 200:
            topic = output.json()
            return topic
        else:
            return f"Error: Received response code {output.status_code}"