app.py

import gradio as gr
import os, json, numpy as np
from agent_core import AgentCore
from graph_reasoner import GraphReasoner
from context_graph import ContextGraph
from graph_view import visualize_reasoned_graph
from memory_store import MemoryStore

# === Lightweight Setup ===
# Redirects model cache to ephemeral folder (won’t bloat storage)
os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
os.environ["HF_HOME"] = "/tmp/hf_home"

# Memory stored in /tmp so it resets safely if needed
MEMORY_PATH = "/tmp/memory.json"

# === Initialize Core Systems ===
agent = AgentCore()
reasoner = GraphReasoner()
graph = ContextGraph()
memory = MemoryStore(path=MEMORY_PATH)

# === Core Chat Function ===
def process_input(user_input, history=[]):
    """Aventra OS conversation loop with memory and reasoning."""
    # 1️⃣ Embed user input
    vector = agent.embed_text(user_input)

    # 2️⃣ Add memory safely
    memory.add_memory(user_input, vector)

    # 3️⃣ Reason on new input
    reasoning = reasoner.reason(user_input)
    response = f"[Aventra Response]: {reasoning}"

    # 4️⃣ Append to chat history
    history.append((user_input, response))
    return history, history

# === Visualize Memory ===
def visualize_memory():
    """Inline visualization for memory graph."""
    memories = memory.memories
    if not memories:
        return gr.HTML.update(value="<b>No memory data yet — start chatting first.</b>")

    relationships = []
    for i, (text1, vec1) in enumerate(memories):
        for j, (text2, vec2) in enumerate(memories):
            if i != j:
                similarity = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
                if similarity > 0.35:
                    relationships.append((text1, text2, float(similarity)))

    # Generate and show visualization
    output_html = visualize_reasoned_graph(memories, relationships)
    print(f"✅ Visualization created at: {output_html}")

    if os.path.exists(output_html):
        with open(output_html, "r") as f:
            html_content = f.read()
        return gr.HTML.update(value=html_content)
    else:
        return gr.HTML.update(value="⚠️ Visualization failed — file not found.")

# === Clear Memory ===
def clear_memory():
    memory.memories = []
    memory.save()
    return "🧹 Memory cleared successfully."

# === Build Gradio Interface ===
with gr.Blocks(title="Aventra OS - Lightweight Mode") as demo:
    gr.Markdown("## 🧠 **Aventra OS — Memory Engine (Light Mode)**")

    chatbot = gr.Chatbot(label="Aventra Chat")
    msg = gr.Textbox(label="Type a message")
    send_btn = gr.Button("Send 🚀")
    visualize_btn = gr.Button("Visualize Memory 🕸️")
    clear_btn = gr.Button("Clear Memory 🧹")
    graph_html = gr.HTML(label="Memory Graph")

    send_btn.click(process_input, inputs=[msg, chatbot], outputs=[chatbot, chatbot])
    visualize_btn.click(visualize_memory, outputs=[graph_html])
    clear_btn.click(clear_memory, outputs=[])

demo.launch(server_name="0.0.0.0", server_port=7860)