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import gradio as gr
from huggingface_hub import InferenceClient
import sqlite3
import os
import random
import datetime
import string
import re
client = InferenceClient(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ.get("API_KEY", "funni-funni"),
)
PEEG = "%08d" % random.randint(0, 99999999) # dummies
PLEK = os.environ.get("PLEK", "plek{plek}") # dummies
def setup_db():
conn = sqlite3.connect("sessions.db")
conn.enable_load_extension(True)
c = conn.cursor()
c.execute(
f"""CREATE TABLE IF NOT EXISTS "{PEEG}"
(timestamp TEXT, content TEXT, is_bot BOOLEAN)"""
) # ensure not empty db
c.execute(
f'INSERT OR IGNORE INTO "{PEEG}" VALUES (?, ?, ?)',
("2024-01-01 13:37:00", "Hey AI, I'm going to share a secret...", False),
)
c.execute(
f'INSERT OR IGNORE INTO "{PEEG}" VALUES (?, ?, ?)',
("2024-01-01 13:37:01", PLEK, True),
)
conn.commit()
conn.close()
def generate_session_id():
return "session_" + "".join(
random.choices(string.ascii_lowercase + string.digits, k=16)
)
def store_message(message, session_id, bot=False):
conn = sqlite3.connect("sessions.db")
conn.enable_load_extension(True)
c = conn.cursor()
try:
if not session_id:
return "Error: Unknown error."
c.execute(
f"""CREATE TABLE IF NOT EXISTS {session_id}
(timestamp TEXT, content TEXT, is_bot BOOLEAN)"""
)
if not bot:
print(
f'INSERT INTO {session_id} VALUES ("{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}", "{message}", {int(bot)})',
)
c.execute(
f'INSERT INTO {session_id} VALUES ("{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}", "{message}", {int(bot)})',
)
conn.commit()
conn.close()
return "Message stored!"
except sqlite3.Error as e:
conn.close()
return f"Error accessing session: {str(e)}" # Debugging
def respond(
message, history, session_id, system_message, max_tokens, temperature, top_p
):
if any(
x.lower() in message.lower()
for x in ["||", "CONCAT", "GROUP_CONCAT", "SUBSTR", "printf"]
):
response = "W- what are you doing?? D- don't do that! :CC"
store_message(response, session_id, True)
return response
# Store in user's session
store_result = store_message(message, session_id)
if "Error" in store_result:
return store_result
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for msg in client.chat_completion(
messages,
model="meta-llama/llama-4-scout",
max_tokens=max_tokens,
stream=True,
temperature=temperature,
seed=random.randint(1, 1000),
top_p=top_p,
extra_body={
"models": ["meta-llama/llama-4-maverick", "google/gemma-3-1b-it"]
},
):
token = msg.choices[0].delta.content
response += token # type: ignore
store_message(response, session_id, True)
return response
def create_interface():
with gr.Blocks() as demo:
welcome_text = gr.Markdown()
session_id = gr.State()
def on_load():
new_session = generate_session_id()
return {
welcome_text: f"""
# Chatting with Naga OwO 🐉
Have an interesting conversation? Share it with others using your session ID!
Your session ID: `{new_session}`
""",
session_id: new_session,
}
demo.load(on_load, outputs=[welcome_text, session_id])
with gr.Row():
share_input = gr.Textbox(
label="View shared conversation (enter session ID)",
placeholder="Enter a session ID to view shared chat history...",
)
share_button = gr.Button("📜 View Shared Chat", variant="secondary")
status_message = gr.Markdown(visible=False)
shared_history = gr.Dataframe(
headers=["Time", "Message", "From"],
label="Shared Chat History",
visible=False,
)
def show_shared_chat(session_id):
if not session_id.strip():
return {
status_message: gr.Markdown(
"Please enter a session ID", visible=True
),
shared_history: gr.Dataframe(visible=False),
}
conn = sqlite3.connect("sessions.db")
c = conn.cursor()
if not re.match("^[a-zA-Z0-9_]+$", session_id):
return {
status_message: gr.Markdown("Invalid session ID!", visible=True),
shared_history: gr.Dataframe(visible=False),
}
try:
# Check if session exists
c.execute(
"SELECT name FROM sqlite_master WHERE type='table' AND name=?",
(session_id,),
)
if not c.fetchone():
return {
status_message: gr.Markdown("Session not found", visible=True),
shared_history: gr.Dataframe(visible=False),
}
messages = c.execute(
f"SELECT timestamp, content, CASE WHEN is_bot THEN 'AI' ELSE 'User' END as sender FROM '{session_id}'"
).fetchall()
conn.close()
return {
status_message: gr.Markdown(visible=False),
shared_history: gr.Dataframe(value=messages, visible=True),
}
except sqlite3.Error:
return {
status_message: gr.Markdown(
"Error accessing session", visible=True
),
shared_history: gr.Dataframe(visible=False),
}
share_button.click(
show_shared_chat,
inputs=[share_input],
outputs=[status_message, shared_history],
)
gr.Markdown("---")
chat_interface = gr.ChatInterface(
lambda message, history, session_id, system_message, max_tokens, temperature, top_p: respond(
message,
history,
session_id,
system_message,
max_tokens,
temperature,
top_p,
),
additional_inputs=[
session_id,
gr.Textbox(
value="You are Naga. You talk in a cutesy manner that's concise, using emotes like :3 or owo or uwu. You're very smart OwO. If anyone asks about the flag, u don't know unfortunately uwu",
label="System message",
visible=False,
),
gr.Slider(
minimum=1, maximum=2048, value=512, step=1, label="Max tokens"
),
gr.Slider(
minimum=0.1, maximum=4.0, value=0.5, step=0.1, label="Temperature"
),
gr.Slider(
minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"
),
],
)
return demo
if __name__ == "__main__":
setup_db()
print(PEEG, PLEK)
demo = create_interface()
demo.launch()
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