Create app.py

app.py

@@ -0,0 +1,113 @@
+# 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()