# --- src/text_processor.py ---
from huggingface_hub import InferenceClient
from pydantic import BaseModel
import os

# Hugging Face Hub client setup
client = InferenceClient(
    "microsoft/Phi-3-mini-4k-instruct",
    token = os.environ['HUGGINGFACE_API_KEY']    
)

# Pydantic class for output validation
class VideoAnalysis(BaseModel):
    indoor: int
    hands_free: int
    screen_interaction: int
    standing: int

def process_description(description):
    # Construct a prompt for your LLM based on the video description
    prompt = f"""
    You are a helpful AI assistant. Analyze the following video description and answer the questions with 0 for True and 1 for False: 

    Video Description: {description}

    Questions:
    - Is the scene indoors? 
    - Are the subject's hands free?
    - Is there screen interaction by the subject?
    - Is the subject standing? 

    Provide your answers in JSON format like this: 
    {{"indoor": 0, "hands_free": 1, "screen_interaction": 0, "standing": 1}} 
    """

    # Using the Hugging Face Hub InferenceClient for text generation
    response = client.chat_completion(
        messages=[{"role": "user", "content": prompt}],
        max_tokens=100,  # Adjust as needed
    )

    # Extract the generated JSON text from the response
    json_text = response.choices[0].message.content

    try:
        # Attempt to parse and validate the JSON response
        analysis_result = VideoAnalysis.model_validate_json(json_text)
        return analysis_result.model_dump_json()  # Return as valid JSON
    except Exception as e:
        print(f"Error processing LLM output: {e}")
        return {"error": "Could not process the video description."}