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# ── Imports & API setup ─────────────────────────────────────────────────────────
import os, requests, gradio as gr
groq_api_key = os.getenv("GROQ_API_KEY")
url, headers = (
"https://api.groq.com/openai/v1/chat/completions",
{"Authorization": f"Bearer {groq_api_key}"}
)
# ── Original core function (unchanged) ──────────────────────────────────────────
def chat_with_groq(user_input): # ← still the same
body = {
"model": "llama-3.1-8b-instant",
"messages": [{"role": "user", "content": user_input}]
}
r = requests.post(url, headers=headers, json=body)
return (r.json()['choices'][0]['message']['content']
if r.status_code == 200 else f"Error: {r.json()}")
# ── UI helpers ─────────────────────────────────────────────────────────────────
CSS = """
#header-row {align-items:center; gap:0.75rem;}
#logo {max-width:60px; border-radius:8px;}
#title {font-size:2rem; font-weight:600; margin:0;}
#left-col {width:64%;}
#right-col {width:35%; padding-left:1rem; border:1px solid #e5e5e5;
border-radius:8px; padding:1rem;}
"""
LOGO_URL = (
"https://raw.githubusercontent.com/Decoding-Data-Science/"
"airesidency/main/dds_logo.jpg"
)
LLM_QUESTIONS = [
"What is Retrieval-Augmented Generation (RAG)?",
"Explain Chain-of-Thought prompting in simple terms.",
"How do I fine-tune an LLM on my own data?",
"What are the security risks of LLM applications?",
"Compare zero-shot vs few-shot prompting.",
"What is the role of vector databases with LLMs?"
]
# ── Build the Gradio app ───────────────────────────────────────────────────────
with gr.Blocks(css=CSS, title="DDS Chat with Groq AI") as demo:
# Header row: logo + title
with gr.Row(elem_id="header-row"):
gr.Image(value=LOGO_URL, elem_id="logo", show_label=False,
show_download_button=False)
gr.Markdown("<div id='title'>DDS Chat with Groq AI (Llama 3.1-8B)</div>")
gr.Markdown("Ask anything—or pick a quick question on the right.")
with gr.Row():
# ── Left column: chat interface ───────────────────────────────────────
with gr.Column(elem_id="left-col"):
chatbot = gr.Chatbot(height=450, label="Conversation")
user_box = gr.Textbox(placeholder="Type your question…",
show_label=False, lines=2)
send_btn = gr.Button("Send", variant="primary")
state = gr.State([])
# ── Right column: quick-question panel ───────────────────────────────
with gr.Column(elem_id="right-col"):
gr.Markdown("**LLM Quick Questions**")
question_dd = gr.Dropdown(choices=LLM_QUESTIONS,
label="Select a question",
interactive=True)
gr.Markdown(
"Pick a topic and it will populate the input box. "
"Feel free to edit before sending."
)
# ── Logic glue ───────────────────────────────────────────────────────────
def respond(user_msg, history):
reply = chat_with_groq(user_msg)
history = history + [(user_msg, reply)]
return history, history
send_btn.click(
fn=respond,
inputs=[user_box, state],
outputs=[chatbot, state],
queue=False
).then(
lambda: "", None, user_box, queue=False # clear textbox
)
question_dd.change(
lambda q: gr.update(value=q),
inputs=question_dd,
outputs=user_box,
queue=False
)
demo.launch()
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