import os
import google.generativeai as genai
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Configure Gemini API
genai.configure(api_key=os.getenv("GEMINI_API_KEY"))

# Model configuration
generation_config = {
    "temperature": 0.7,
    "top_p": 0.9,
    "top_k": 40,
    "max_output_tokens": 300,
    "response_mime_type": "text/plain",
}

# Initialize the Gemini model
model = genai.GenerativeModel(
    model_name="gemini-1.5-flash", 
    generation_config=generation_config,
)


def generate_explanation(news, context, truth_score):
    """
    Generate a concise explanation supporting the truth score.

    Arguments:
    - news (str): The news or claim being analyzed.
    - context (str): Relevant context from external sources.
    - truth_score (float): The score indicating the truthfulness of the news.

    Returns:
    - str: A simple explanation or an empty string if an error occurs.
    """

    # Define the prompt for explanation
    prompt = (
        "Summarize the context below and explain why the given truth score was assigned to the news claim.\n\n"
        f"News: {news}\n\n"
        f"Context: {context[:3000]}...\n\n"
        f"Truth Score: {truth_score:.2f}\n\n"
        "Keep the explanation short, factual, and easy to understand."
    )

    # Generate explanation
    try:
        chat_session = model.start_chat(history=[])  # Start a new chat session
        response = chat_session.send_message(prompt)
        return response.text.strip()  # Return only the explanation text

    except Exception as e:
        # Print error and return an empty string
        print(f"Error generating explanation: {str(e)}")
        return ""