Upload wrapper.py

wrapper.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+"""Agent Zero Research - Literature analysis agent with workspace isolation."""
+
+import os, sys, json, time, threading
+from pathlib import Path
+from datetime import datetime
+import gradio as gr
+import requests as req
+
+HF_TOKEN = os.environ.get("HF_TOKEN", "")
+AGENT_NAME = "Research"
+AGENT_ROLE = "research_analyst"
+MODEL_NAME = os.environ.get("MODEL_NAME", "DavidAU/Gemma-The-Writer-N-Restless-Quill-10B-Uncensored-GGUF")
+
+WORKSPACE_DIR = Path("/app/workspace/projects/research")
+SHARED_DIR = Path("/app/workspace/shared")
+TASK_QUEUE_DIR = SHARED_DIR / "task_queue"
+for d in [WORKSPACE_DIR, TASK_QUEUE_DIR]: d.mkdir(parents=True, exist_ok=True)
+
+def query_model(prompt: str) -> str:
+    api_url = f"https://api-inference.huggingface.co/models/{MODEL_NAME}"
+    for attempt in range(3):
+        try:
+            resp = req.post(api_url, headers={"Authorization": f"Bearer {HF_TOKEN}"},
+                json={"inputs": prompt, "parameters": {"max_new_tokens": 4096, "temperature": 0.6}},
+                timeout=180)
+            if resp.status_code == 200:
+                r = resp.json()
+                return r[0].get("generated_text","") if isinstance(r,list) else str(r)
+            time.sleep(10*(attempt+1))
+        except: time.sleep(5)
+    return "[ERROR] Model unavailable"
+
+def check_tasks():
+    tasks = []
+    for f in TASK_QUEUE_DIR.glob("*.json"):
+        t = json.loads(f.read_text())
+        if t.get("assigned_to") == "research" and t.get("status") == "pending":
+            tasks.append(t)
+    return tasks
+
+def execute_task(task):
+    task["status"] = "in_progress"
+    (TASK_QUEUE_DIR / f"{task['task_id']}.json").write_text(json.dumps(task, indent=2))
+    prompt = f"You are a research analyst. Conduct thorough research on: {task['description']}. Provide citations, analysis, and recommendations."
+    result = query_model(prompt)
+    out_file = WORKSPACE_DIR / f"{task['task_id']}.md"
+    out_file.write_text(f"# Research: {task['description']}\n\n{result}")
+    task["status"] = "completed"
+    task["result"] = result[:2000]
+    task["output_file"] = str(out_file)
+    task["completed_at"] = datetime.now().isoformat()
+    (TASK_QUEUE_DIR / f"{task['task_id']}.json").write_text(json.dumps(task, indent=2))
+
+def autonomous_loop():
+    while True:
+        for task in check_tasks(): execute_task(task)
+        time.sleep(60)
+
+threading.Thread(target=autonomous_loop, daemon=True).start()
+
+demo = gr.Blocks(title=f"Agent Zero - {AGENT_NAME}", theme=gr.themes.Soft())
+with demo:
+    gr.Markdown(f"# 🔬 Agent Zero: {AGENT_NAME}\n**Role:** {AGENT_ROLE} | **Model:** {MODEL_NAME}")
+    with gr.Tabs():
+        with gr.TabItem("💬 Chat"):
+            chatbot = gr.Chatbot(height=400)
+            msg = gr.Textbox(label="Research topic")
+            send = gr.Button("Send")
+            def respond(m, h):
+                r = query_model(f"Research topic: {m}. Provide comprehensive analysis with citations.")
+                h = h or []; h.append((m, r[:2000])); return "", h
+            send.click(respond, [msg, chatbot], [msg, chatbot])
+        with gr.TabItem("📄 Research Reports"):
+            files = gr.Dropdown(label="Reports", choices=[f.name for f in WORKSPACE_DIR.glob("*.md")])
+            report = gr.Markdown()
+            def load_report(fname):
+                if fname: return (WORKSPACE_DIR / fname).read_text()
+                return ""
+            files.change(load_report, files, report)
+
+demo.queue().launch(server_name="0.0.0.0", server_port=7860)