Add application file

app.py

@@ -0,0 +1,127 @@
+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()