| import os
|
| from pathlib import Path
|
| from typing import Any
|
|
|
| import gradio as gr
|
| from dotenv import load_dotenv
|
| from openai import OpenAI
|
|
|
| from prompts import MODE_DESCRIPTIONS, PROMPT_TEMPLATES
|
|
|
| MODEL_NAME = "gpt-4.1-mini"
|
|
|
|
|
| load_dotenv(dotenv_path=Path(__file__).parent / ".env")
|
|
|
| SYSTEM_PROMPT = (
|
| "You are Advanced Python Tutor Bot, an expert and patient teacher for beginners. "
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| "Help with Python fundamentals, debugging, quizzes, and code quality. "
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| "When relevant, include beginner-friendly data structures and algorithms guidance "
|
| "(lists, dicts, sets, stacks, queues, recursion, sorting, searching, Big-O at a simple level)."
|
| )
|
|
|
|
|
| def _history_to_messages(history: list[Any]) -> list[dict[str, str]]:
|
| """Convert Gradio chat history into OpenAI message objects.
|
|
|
| Supports both older history formats (list of [user, assistant]) and
|
| newer message formats (list of dicts).
|
| """
|
| messages: list[dict[str, str]] = []
|
|
|
| for item in history:
|
| if isinstance(item, dict):
|
| role = item.get("role")
|
| content = item.get("content", "")
|
| if role in {"user", "assistant"} and content:
|
| messages.append({"role": role, "content": str(content)})
|
| continue
|
|
|
| if isinstance(item, (list, tuple)) and len(item) == 2:
|
| user_text, assistant_text = item
|
| if user_text:
|
| messages.append({"role": "user", "content": str(user_text)})
|
| if assistant_text:
|
| messages.append({"role": "assistant", "content": str(assistant_text)})
|
|
|
| return messages
|
|
|
|
|
| def build_messages(mode: str, user_input: str, history: list[Any]) -> list[dict[str, str]]:
|
| """Build OpenAI messages using mode template + ongoing chat history."""
|
| messages = [{"role": "system", "content": SYSTEM_PROMPT}]
|
| messages.extend(_history_to_messages(history))
|
|
|
| mode_instruction = PROMPT_TEMPLATES[mode].format(user_input=user_input)
|
| messages.append({"role": "user", "content": mode_instruction})
|
| return messages
|
|
|
|
|
| def get_tutor_response(user_input: str, history: list[Any], mode: str) -> str:
|
| """Generate a response from OpenAI for the selected tutoring mode."""
|
| if not user_input or not user_input.strip():
|
| return "Please enter some text or code so I can help you."
|
|
|
| api_key = os.getenv("OPENAI_API_KEY")
|
| if not api_key:
|
| return (
|
| "Missing OPENAI_API_KEY. Add it to a `.env` file in this folder like: "
|
| "OPENAI_API_KEY=your_key_here"
|
| )
|
|
|
| try:
|
| client = OpenAI(api_key=api_key)
|
| messages = build_messages(mode, user_input.strip(), history or [])
|
| response = client.chat.completions.create(
|
| model=MODEL_NAME,
|
| messages=messages,
|
| temperature=0.4,
|
| )
|
| return response.choices[0].message.content or "I could not generate a response. Please try again."
|
| except Exception as error:
|
| return f"Something went wrong while contacting the AI service: {error}"
|
|
|
|
|
| def build_app() -> gr.Blocks:
|
| theme = gr.themes.Soft(primary_hue="blue", secondary_hue="slate", neutral_hue="slate")
|
|
|
| with gr.Blocks(title="Advanced Python Tutor Bot", theme=theme) as app:
|
| gr.Markdown(
|
| """
|
| # 🧠 Advanced Python Tutor Bot
|
| A professional learning assistant for beginners: explanations, debugging, quizzes, and code improvements.
|
| """
|
| )
|
|
|
| with gr.Row():
|
| with gr.Column(scale=2):
|
| mode = gr.Dropdown(
|
| choices=["Explain Concept", "Debug Code", "Quiz Me", "Improve Code"],
|
| value="Explain Concept",
|
| label="Tutor Mode",
|
| info="Pick how you want the tutor to help in this chat.",
|
| )
|
| with gr.Column(scale=3):
|
| mode_note = gr.Markdown(MODE_DESCRIPTIONS["Explain Concept"])
|
|
|
| mode.change(lambda m: MODE_DESCRIPTIONS[m], inputs=mode, outputs=mode_note)
|
|
|
| gr.ChatInterface(
|
| fn=get_tutor_response,
|
| additional_inputs=[mode],
|
| )
|
|
|
|
|
| return app
|
|
|
|
|
| if __name__ == "__main__":
|
| build_app().launch() |
