import os
import requests
from dotenv import load_dotenv

def main():
    # 1. Load the environment file with your API key
    home_dir = os.path.expanduser("~")
    env_file = os.path.join(home_dir, ".secretlab.env")
    load_dotenv(env_file)
    api_key = os.getenv("SECRETLAB_API_KEY")

    if not api_key:
        raise ValueError("SECRETLAB_API_KEY not found in ~/.secretlab.env")

    # 2. The correct endpoint
    url = "https://chat.secretlab.page/api/chat/completions"

    # 3. Sentences to classify
    sentences = [
        "I love sunny days!",
        "This is a bland sandwich.",
        "I really hate waiting in long lines.",
        "The meeting was okay, nothing special.",
        "I am so excited about the upcoming holidays!"
    ]

    # 4. For each sentence, send a separate request
    for index, sentence in enumerate(sentences, start=1):
        # Prepare the request payload
        # We use Chat Completion format, but the system message strictly instructs the assistant
        # to return only "Positive" or "Negative" with no extra explanation.
        payload = {
            "model": "deepseek-r1:32b",
            "messages": [
                {
                    "role": "system",
                    "content": (
                        "You are a helpful text classifier. You must respond with only "
                        "'Positive' or 'Negative' for the given text. No other words or punctuation."
                    )
                },
                {
                    "role": "user",
                    "content": f"Classify this sentence as Positive or Negative:\n{sentence}"
                }
            ],
            "temperature": 0.0
        }

        # Set the headers, including authorization
        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {api_key}"
        }

        # POST the request
        response = requests.post(url, headers=headers, json=payload)

        # Check for success
        if response.status_code == 200:
            completion = response.json()
            # Extract the assistant's response text
            result_text = completion["choices"][0]["message"]["content"].strip()

            # Print the classification result for each sentence
            print(f"Sentence {index}: {sentence}")
            print(f"Classification: {result_text}")
            print("---------")
        else:
            print(f"Request for sentence {index} failed with status code: {response.status_code}")
            print("Response:", response.text)
            print("---------")

if __name__ == "__main__":
    main()