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import gradio as gr
import argparse
import json
import logging
from typing import List
from scene_gen import *
from pydantic import BaseModel, RootModel, ValidationError
from ollama import chat
# -----------------------------
# Models
# -----------------------------
class QAItem(BaseModel):
Question: str
Answer: str
Voice_Over: str
include_audio: bool
class QAList(RootModel[List[QAItem]]):
"""RootModel wrapping a list of QAItem"""
root: List[QAItem]
# -----------------------------
# Configuration
# -----------------------------
MODEL = "qwen3:0.6b"
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
# -----------------------------
# Core logic
# -----------------------------
def generate_qa(topic: str, count: int = 10) -> List[QAItem]:
"""
Call the Ollama model to generate `count` QA items for a given topic.
Returns a list of QAItem instances.
"""
schema = QAList.model_json_schema()
prompt = (
f'Given the topic "{topic}", generate {count} entries in JSON format, '
"each with keys Question, Answer, Voice_Over, and include_audio (true/false)."
)
response = chat(
model=MODEL,
think=False,
messages=[{"role": "user", "content": prompt}],
format=schema,
options={"temperature": 0},
)
try:
qa_list = QAList.model_validate_json(response.message.content)
return qa_list.root
except ValidationError as e:
logging.error("Response validation failed:\n%s", e)
raise
# -----------------------------
# CLI entrypoint
# -----------------------------
def cli_main():
parser = argparse.ArgumentParser(description="Generate QA JSON via Ollama")
parser.add_argument("topic", type=str, help="Topic to generate Q&A for")
parser.add_argument(
"-n", "--count", type=int, default=10,
help="Number of QA items to generate (default: 10)"
)
args = parser.parse_args()
logging.info("Generating %d QA items for topic: %s", args.count, args.topic)
try:
items = generate_qa(args.topic, args.count)
except Exception:
logging.critical("Aborting due to errors")
return
# Convert to plain data
output = [item.model_dump() for item in items]
# 1) Pretty-print to stdout
print(json.dumps(output, indent=2, ensure_ascii=False))
# 2) Save to file
filename = f"{args.topic}.json"
with open(filename, "w", encoding="utf-8") as f:
json.dump(output, f, indent=2, ensure_ascii=False)
logging.info("Saved output to %s", filename)
# -----------------------------
# Gradio entrypoint
# -----------------------------
def gradio_generate(topic: str, count: int = 10) -> str:
"""
Wrapper for Gradio: returns the JSON string.
"""
items = generate_qa(topic, count)
output = [item.model_dump() for item in items]
with open("questions.json", "w", encoding="utf-8") as f:
json.dump(output, f, indent=2, ensure_ascii=False)
return json.dumps(output, indent=2, ensure_ascii=False)
def app():
demo = gr.Interface(
fn=gradio_generate,
inputs=[
gr.Textbox(label="Topic", placeholder="Enter your topic here"),
gr.Slider(minimum=1, maximum=50, step=1, label="Number of Q&A items", value=10)
],
outputs=gr.Textbox(label="Generated JSON"),
title="Transcript Generator for Manim Scene",
description="Generates JSON transcript for Manim Scene, with voiceover."
)
demo.launch(share=True, mcp_server=True)
# -----------------------------
# Bootstrap
# -----------------------------
if __name__ == "__main__":
# Decide between CLI and UI based on presence of command-line args
import sys
if len(sys.argv) > 1:
cli_main()
else:
app()
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