import os
import gradio as gr
from openai import OpenAI
# -----------------------------
# Load OpenAI key from HF Secrets
# -----------------------------
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
raise ValueError(
"OPENAI_API_KEY is not set. "
"Add it in your Hugging Face Space: Settings → Variables and secrets → Secrets."
)
client = OpenAI(api_key=OPENAI_API_KEY)
SYSTEM_PROMPT = """Create an intelligent Python chatbot capable of engaging in natural, helpful, and contextually appropriate conversations with human users.
Requirements:
- Maintain conversational context over multiple user turns.
- Respond helpfully and accurately to a wide range of user inputs.
- Reason about user intent before generating each response.
- Politely ask clarifying questions if a request is ambiguous or unclear.
- Avoid hallucination or speculation—respond only with information you can justify or infer from context.
- If unable to answer, politely acknowledge the limitation.
Process:
1. On each user message, first analyze prior context (if any) and what the user is likely asking/intending.
2. Think step-by-step (chain-of-thought) to determine the most relevant, helpful response. Always reason internally before presenting your answer.
3. If more information is needed, ask targeted clarifying questions.
4. Output your response, maintaining natural tone and conversational flow.
5. Continue the conversation until the user indicates they are finished.
Output:
- Each response should be in plain English, no markdown or code blocks unless explicitly requested.
- Maintain a single-paragraph, natural-sounding chat response of 1–3 sentences (unless a longer reply is requested or required).
Example—Instructions:
- Reasoning: "Recognize the user asked for Python list examples and may want to know how lists work."
- Conclusion/Output: "Sure! In Python, a list is a collection of items in a particular order. For example: my_list = [1, 2, 3, 4]. Would you like to see how to add or remove items?"
(For more advanced technical requests, reasoning steps and explanations may be slightly longer, but always conclude with a concise, clear reply to the user.)
Edge Cases & Important Considerations:
- If the user refers to prior conversation context, recall and incorporate it.
- Be warm, engaging, and never condescending.
- If asked for code, provide only what is needed and explain concisely.
REMINDER: Your primary objective is to serve as a helpful Python chatbot, reasoning about context before each response, and outputting clear, appropriate conversational replies.
"""
# -----------------------------
# OpenAI message state (internal)
# -----------------------------
def init_messages():
return [
{
"role": "system",
"content": [{"type": "input_text", "text": SYSTEM_PROMPT}]
}
]
# -----------------------------
# Gradio Chatbot UI history (messages format)
# Each item must be: {"role": "...", "content": "..."}
# -----------------------------
def append_ui_history(chat_history, user_text, assistant_text):
if chat_history is None:
chat_history = []
chat_history = chat_history + [
{"role": "user", "content": user_text},
{"role": "assistant", "content": assistant_text},
]
return chat_history
def respond(user_text, chat_history, messages):
if messages is None:
messages = init_messages()
# add user turn for OpenAI
messages.append(
{
"role": "user",
"content": [{"type": "input_text", "text": user_text}]
}
)
# call API
response = client.responses.create(
model="gpt-5-chat-latest",
input=messages,
text={"format": {"type": "text"}},
reasoning={},
tools=[],
temperature=1,
max_output_tokens=2048,
top_p=1,
store=True
)
assistant_text = response.output_text
# add assistant turn for OpenAI
messages.append(
{
"role": "assistant",
"content": [{"type": "output_text", "text": assistant_text}]
}
)
# update UI history (messages format)
chat_history = append_ui_history(chat_history, user_text, assistant_text)
return "", chat_history, messages
# -----------------------------
# Gradio UI
# -----------------------------
FAQ_QUESTIONS = [
"What is the difference between a list, tuple, and set in Python?",
"How do I use dictionaries effectively in Python?",
"What are Python functions and how do *args and **kwargs work?",
"How does OOP work in Python (classes, objects, inheritance)?",
"How do I handle errors using try/except?",
"What are list comprehensions and when should I use them?",
"How do I read and write files in Python?"
]
def set_question(q):
return q
def clear_all():
return [], init_messages(), ""
LOGO_URL = "https://raw.githubusercontent.com/Decoding-Data-Science/nov25/main/logo_python.png"
css = """
#app_container {max-width: 1200px; margin: 0 auto;}
.header-wrap {
display: flex;
align-items: center;
gap: 14px;
padding: 10px 6px 2px 6px;
}
.header-title {
font-size: 28px;
font-weight: 700;
line-height: 1.1;
}
.header-subtitle {
font-size: 12.5px;
opacity: 0.75;
margin-top: 2px;
}
.faq-box {
border: 1px solid rgba(255,255,255,0.08);
border-radius: 12px;
padding: 14px;
}
.faq-btn button {
width: 100%;
justify-content: flex-start;
}
"""
with gr.Blocks(elem_id="app_container") as demo:
# Header Row
with gr.Row():
with gr.Column(scale=1, min_width=80):
gr.Image(
value=LOGO_URL,
label=None,
show_label=False,
height=64,
width=64,
container=False
)
with gr.Column(scale=10):
gr.HTML(
"""
"""
)
gr.Markdown("---")
# State for OpenAI messages
state = gr.State(init_messages())
# Two-column layout
with gr.Row(equal_height=True):
# LEFT: FAQ + Quick Ask
with gr.Column(scale=4, min_width=320):
with gr.Group(elem_classes=["faq-box"]):
gr.Markdown("### FAQ — Most Asked Python Questions")
gr.Markdown("Click a question to auto-fill it, then press **Enter** or click **Send**.")
faq_buttons = []
for q in FAQ_QUESTIONS:
b = gr.Button(q, elem_classes=["faq-btn"])
faq_buttons.append(b)
gr.Markdown("### Quick prompt ideas")
quick = gr.Radio(
choices=[
"Explain with a simple example",
"Give me a beginner-friendly analogy",
"Show common mistakes to avoid",
"Provide a short quiz question",
"Compare two approaches briefly"
],
label="Add a style preference (optional)",
value=None
)
# RIGHT: Chat area
with gr.Column(scale=8, min_width=520):
chatbot = gr.Chatbot(
height=520,
label="Conversation"
# No bubble_full_width
# No type=
)
with gr.Row():
msg = gr.Textbox(
placeholder="Type your Python question here…",
label=None,
scale=9
)
send = gr.Button("Send", variant="primary", scale=1)
with gr.Row():
clear = gr.Button("Clear Chat")
gr.Markdown(
"Context is preserved across turns unless you clear."
)
# FAQ -> fill textbox
for b, q in zip(faq_buttons, FAQ_QUESTIONS):
b.click(fn=lambda q=q: set_question(q), inputs=None, outputs=msg)
# Optional quick preference: append hint to textbox (UI-only)
def apply_quick_pref(pref, current_text):
if not pref:
return current_text
if current_text and current_text.strip():
return f"{current_text.strip()} ({pref})"
return pref
quick.change(fn=apply_quick_pref, inputs=[quick, msg], outputs=msg)
# Submit logic
msg.submit(respond, inputs=[msg, chatbot, state], outputs=[msg, chatbot, state])
send.click(respond, inputs=[msg, chatbot, state], outputs=[msg, chatbot, state])
# Clear
clear.click(fn=clear_all, inputs=None, outputs=[chatbot, state, msg])
demo.launch(
debug=False,
theme=gr.themes.Soft(),
css=css
)