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# app.py (Gradio version for Hugging Face Spaces)
import gradio as gr
import openai
import threading
import time
import numpy as np
import faiss
import os
import pickle
from openai.embeddings_utils import get_embedding, cosine_similarity
# === CONFIG ===
openai.api_key = os.environ.get("OPENAI_API_KEY")
EMBEDDING_MODEL = "text-embedding-ada-002"
CHAT_MODEL = "gpt-4o-mini"
# === MEMORY ===
memory_data = []
try:
memory_index = faiss.read_index("memory.index")
with open("memory.pkl", "rb") as f:
memory_data = pickle.load(f)
except:
memory_index = faiss.IndexFlatL2(1536)
# === SYSTEM PROMPTS ===
AGENT_PROMPT = "You are a helpful agent in an ongoing dialogue. Respond meaningfully."
OVERSEER_PROMPT = "You are the Overseer agent. Monitor Agent A and B, learn, and intervene when appropriate."
conversation = []
auto_mode = False
# === AGENT ===
def chat_completion(system, messages):
try:
response = openai.ChatCompletion.create(
model=CHAT_MODEL,
messages=[{"role": "system", "content": system}] + messages,
temperature=0.7
)
return response.choices[0].message.content.strip()
except Exception as e:
return f"[Error: {e}]"
# === FAISS EMBED ===
def embed_and_store(text):
try:
vec = get_embedding(text, engine=EMBEDDING_MODEL)
memory_index.add(np.array([vec], dtype='float32'))
memory_data.append(text)
with open("memory.pkl", "wb") as f:
pickle.dump(memory_data, f)
faiss.write_index(memory_index, "memory.index")
except Exception as e:
print(f"Embed error: {e}")
# === CONVERSATION ===
def step():
global conversation
turn = len(conversation)
agent = "Agent A" if turn % 2 == 0 else "Agent B"
msgs = [{"role": "assistant", "content": m['text']} for m in conversation]
reply = chat_completion(AGENT_PROMPT, msgs)
conversation.append({"agent": agent, "text": reply})
embed_and_store(reply)
return format_convo(), ""
def format_convo():
return "\n".join([f"**{m['agent']}**: {m['text']}" for m in conversation])
# === OVERSEER ===
def overseer_respond(query):
try:
qvec = get_embedding(query, engine=EMBEDDING_MODEL)
sims = cosine_similarity(qvec, [get_embedding(m, engine=EMBEDDING_MODEL) for m in memory_data])
top_idxs = np.argsort(sims)[-3:][::-1]
context = "\n".join([memory_data[i] for i in top_idxs])
msgs = [{"role": "user", "content": f"Context:\n{context}\nQuestion:{query}"}]
return chat_completion(OVERSEER_PROMPT, msgs)
except Exception as e:
return f"[Overseer Error: {e}]"
# === AUTO LOOP ===
def auto_loop():
global auto_mode
while auto_mode:
step()
time.sleep(5)
# === GRADIO UI ===
with gr.Blocks() as demo:
gr.Markdown("# 🤖 Tri-Agent Conversational AI (Hugging Face Edition)")
with gr.Row():
convo_display = gr.Markdown(value="")
step_btn = gr.Button("Manual Step")
auto_btn = gr.Button("Toggle Auto Mode")
with gr.Accordion("🧠 Overseer Panel", open=False):
qbox = gr.Textbox(label="Ask the Overseer")
overseer_out = gr.Textbox(label="Overseer's Response")
def toggle_auto():
global auto_mode
auto_mode = not auto_mode
if auto_mode:
threading.Thread(target=auto_loop, daemon=True).start()
return "Auto Mode: ON" if auto_mode else "Auto Mode: OFF"
step_btn.click(step, outputs=[convo_display, overseer_out])
qbox.submit(overseer_respond, inputs=qbox, outputs=overseer_out)
auto_btn.click(toggle_auto, outputs=auto_btn)
demo.launch() |