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# core/script_generator.py
import asyncio
import uuid
from typing import List, Dict
from config import OPENROUTER_API_KEY
from core.seed_manager import SeedManager
import httpx
import json
# Initialize seed manager
seed_manager = SeedManager()
# ---------------- OPENROUTER LLM CALL ----------------
async def _call_openrouter_llm(prompt: str) -> str:
"""
Calls OpenRouter LLM to generate proposed video script.
Returns the raw text script.
"""
url = "https://api.openrouter.ai/v1/chat/completions"
headers = {"Authorization": f"Bearer {OPENROUTER_API_KEY}"}
payload = {
"model": "gpt-4.1-mini", # powerful and suitable for script generation
"messages": [
{"role": "system", "content": "You are a professional creative video script writer."},
{"role": "user", "content": prompt}
],
"max_tokens": 1500,
"temperature": 0.7
}
async with httpx.AsyncClient(timeout=60) as client:
response = await client.post(url, json=payload, headers=headers)
response.raise_for_status()
data = response.json()
# OpenRouter returns message content in choices[0].message.content
return data["choices"][0]["message"]["content"]
# ---------------- SCRIPT PROCESSING ----------------
def parse_script_to_scenes(script_text: str) -> List[Dict]:
"""
Converts a script text into scene + keyframe JSON.
Each scene may have multiple keyframes.
Assigns unique scene_ids and seeds.
"""
scenes_json = []
scene_counter = 1
keyframe_counter = 1
lines = [line.strip() for line in script_text.split("\n") if line.strip()]
for line in lines:
# Generate a unique scene_id and seed for this scene
scene_id = scene_counter
seed = seed_manager.generate_seed(scene_id)
# We assume each line is a keyframe
scenes_json.append({
"scene": scene_counter,
"scene_id": scene_id,
"keyframe_number": keyframe_counter,
"description": line,
"camera": "default", # can be improved later
"seed": seed
})
keyframe_counter += 1
scene_counter += 1
return scenes_json
# ---------------- MAIN FUNCTION ----------------
async def generate_script_async(idea: str, user_confirmed: bool = True) -> List[Dict]:
"""
Full pipeline for script generation:
1. Generates proposed script from LLM
2. Waits for user confirmation
3. Converts confirmed script into scene + keyframe JSON
"""
prompt = f"Create a professional video script for: {idea}. Write each scene in one line."
raw_script = await _call_openrouter_llm(prompt)
# Here you can integrate actual user confirmation in your frontend
if not user_confirmed:
return [{"proposed_script": raw_script}]
# Convert approved script into structured scene/keyframe JSON
scenes = parse_script_to_scenes(raw_script)
return scenes
def generate_script(idea: str, user_confirmed: bool = True) -> List[Dict]:
"""
Synchronous wrapper for pipeline integration.
"""
return asyncio.get_event_loop().run_until_complete(
generate_script_async(idea, user_confirmed)
)
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