import json
from app.core.ai import ai_client
from app.schemas.script import ScriptAnalysisResponse

class ScriptParserService:
    async def parse_script(self, text_content: str) -> ScriptAnalysisResponse:
        prompt = f"""
        You are an expert Assistant Director and Script Supervisor.
        Analyze the following screenplay text and break it down into Scenes and Shots.
        
        For each Scene, identify:
        - Scene Number (if present, or incrementing)
        - Heading (INT./EXT. LOCATION - DAY/NIGHT)
        - Brief Description of what happens
        
        For each Scene, break the action down into a list of Shots (Camera setups).
        For each Shot, provide:
        - Shot Number (e.g. 1, 1A, etc)
        - Description of the action in the shot
        - Visual Notes (Camera angles, movement if implied)
        - Dialogue (if any covers this shot)

        Output MUST be a valid JSON object matching this structure:
        {{
            "scenes": [
                {{
                    "scene_number": "1",
                    "heading": "INT. OFFICE - DAY",
                    "description": "John sits at his desk.",
                    "shots": [
                        {{
                            "shot_number": "1A",
                            "description": "Wide shot of John at desk.",
                            "visual_notes": "Static",
                            "dialogue": null
                        }}
                    ]
                }}
            ]
        }}

        SCRIPT TEXT:
        {text_content}
        """

        json_str = await ai_client.generate_json(prompt)
        
        # Parse JSON and validate with Pydantic
        try:
            data = json.loads(json_str)
            return ScriptAnalysisResponse(**data)
        except json.JSONDecodeError:
            # Fallback or retry logic could go here
            raise ValueError("Failed to parse LLM response as JSON")

parser_service = ScriptParserService()