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app.py

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+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 environment variables from a local .env file if it exists.
+load_dotenv(dotenv_path=Path(__file__).parent / ".env")
+
+SYSTEM_PROMPT = (
+    "You are Advanced Python Tutor Bot, an expert and patient teacher for beginners. "
+    "Help with Python fundamentals, debugging, quizzes, and code quality. "
+    "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()

prompts.py

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+PROMPT_TEMPLATES = {
+    "Explain Concept": (
+        "Explain this Python concept for a beginner using simple words, a short example, "
+        "and a quick summary at the end:\n\n{user_input}"
+    ),
+    "Debug Code": (
+        "Help debug this Python code. Identify likely errors, explain why they happen, and "
+        "show a corrected version. End with a short prevention checklist:\n\n{user_input}"
+    ),
+    "Quiz Me": (
+        "Run a short interactive Python quiz for a beginner based on this topic. "
+        "Ask one question at a time, wait for the learner's answer, then give feedback and continue:\n\n{user_input}"
+    ),
+    "Improve Code": (
+        "Improve this Python code for readability, style, and beginner-friendly best practices. "
+        "Explain each improvement clearly and show before/after snippets when possible:\n\n{user_input}"
+    ),
+}
+
+MODE_DESCRIPTIONS = {
+    "Explain Concept": "**Explain Concept**: Learn a topic with plain-English explanations and examples.",
+    "Debug Code": "**Debug Code**: Find errors, understand causes, and get corrected code.",
+    "Quiz Me": "**Quiz Me**: Practice with a guided one-question-at-a-time mini quiz.",
+    "Improve Code": "**Improve Code**: Refactor code for readability, structure, and best practices.",
+}

requirements.txt

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+gradio
+openai