Create app.py

app.py

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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 creation
+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, device_map="auto")
+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").to(model.device)
+    gen = model.generate(
+        **inputs,
+        max_new_tokens=max_tokens,
+        temperature=temp,
+        do_sample=True
+    )
+    return tokenizer.decode(gen[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):
+    # Create a temp PDF with chat content
+    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:
+        line = f"[{msg['timestamp']}] {msg['user']}: {msg['user_msg']}"
+        textobj.textLine(line)
+        line2 = f"   Assistant {msg['icon']}: {msg['bot_msg']}"
+        textobj.textLine(line2)
+        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, step=10, 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, max_toks):
+        new_hist = respond(message, history, user, temp, 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()