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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
from collections import defaultdict
from datetime import datetime
import pandas as pd
# PDF export
from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import letter
import tempfile
# Model & sentiment
MODEL_NAME = "HuggingFaceTB/SmolLM2-360M-Instruct"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME) # NO device_map here
sentiment_analyzer = pipeline("sentiment-analysis")
SYSTEM_PROMPT = "You are a friendly assistant with fire vibes."
feedback_store = []
like_leaderboard = defaultdict(int)
def now_str():
return datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC")
def generate_reply(prompt_str, temp, max_tokens):
inputs = tokenizer(prompt_str, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temp,
do_sample=True
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
def respond(message, history, user, temp, max_tokens):
username = user.name if user else "anonymous"
prompt = SYSTEM_PROMPT + "\n"
for msg_info in history:
prompt += f"[{msg_info['timestamp']}] {msg_info['user']}: {msg_info['user_msg']}\n"
prompt += f"[{msg_info['timestamp']}] 🧠 {msg_info['bot_msg']}\n"
prompt += f"[{now_str()}] {username}: {message}\nAssistant:"
bot_reply = generate_reply(prompt, temp, max_tokens)
sentiment = sentiment_analyzer(bot_reply)[0]
icon = "😊" if sentiment["label"] == "POSITIVE" else "😐" if sentiment["label"] == "NEUTRAL" else "☹️"
record = {
"timestamp": now_str(),
"user": username,
"user_msg": message,
"bot_msg": bot_reply,
"sentiment": sentiment,
"icon": icon
}
history.append(record)
return history
def record_feedback(history, fb):
if history:
last = history[-1]
feedback_store.append({
"timestamp": last["timestamp"],
"user_msg": last["user_msg"],
"bot_msg": last["bot_msg"],
"sentiment": last["sentiment"],
"feedback": fb
})
if fb == "like":
like_leaderboard[last["bot_msg"]] += 1
return history
def download_csv():
df = pd.DataFrame(feedback_store)
path = "/tmp/sentiment_feedback.csv"
df.to_csv(path, index=False)
return path
def leaderboard_text():
sorted_leader = sorted(like_leaderboard.items(), key=lambda x: x[1], reverse=True)
lines = [f"{i+1}. {msg[:60]}... → {count} likes" for i,(msg,count) in enumerate(sorted_leader)]
return "\n".join(lines)
def export_pdf(history):
temp_pdf = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf").name
c = canvas.Canvas(temp_pdf, pagesize=letter)
textobj = c.beginText(40, 750)
textobj.setFont("Helvetica", 10)
for msg in history:
textobj.textLine(f"[{msg['timestamp']}] {msg['user']}: {msg['user_msg']}")
textobj.textLine(f" 🧠 {msg['icon']}: {msg['bot_msg']}")
textobj.textLine("")
c.drawText(textobj)
c.save()
return temp_pdf
with gr.Blocks() as demo:
gr.Markdown("## 🔥 Smol Chatbot 🔥")
login_button = gr.LoginButton()
history_state = gr.State([])
with gr.Row():
temp_slider = gr.Slider(0.1, 1.2, value=0.7, label="Temperature")
max_tokens_slider = gr.Slider(20, 300, value=150, label="Max Tokens")
msg = gr.Textbox(label="Your message")
send = gr.Button("Send")
like = gr.Button("👍 Like")
dislike = gr.Button("👎 Dislike")
download_csv_btn = gr.Button("📥 Download CSV")
download_pdf_btn = gr.Button("📄 Download PDF")
leaderboard_btn = gr.Button("🏆 Leaderboard")
leaderboard_out = gr.Textbox(label="Leaderboard")
def on_send(message, history, user, temp_val, max_toks):
new_hist = respond(message, history, user, temp_val, max_toks)
return "", new_hist
send.click(
on_send,
[msg, history_state, login_button, temp_slider, max_tokens_slider],
[msg, history_state],
)
like.click(record_feedback, history_state, history_state, _js="() => ['like']")
dislike.click(record_feedback, history_state, history_state, _js="() => ['dislike']")
download_csv_btn.click(download_csv, None, gr.File())
download_pdf_btn.click(export_pdf, history_state, gr.File())
leaderboard_btn.click(lambda: leaderboard_text(), None, leaderboard_out)
demo.launch()
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