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from llama_cpp import Llama
from duckduckgo_search import DDGS
from e2b import Sandbox
import gradio as gr
# Load GGUF model (Sam-reason-S2.1, gemma3 arch)
llm = Llama(
model_path="models/Sam-reason-S2.1.Q4_K_S.gguf",
n_ctx=2048,
verbose=False
)
# Tools
def search_tool(q):
with DDGS() as ddgs:
results = ddgs.text(q)
return "\n".join([f"{r['title']}: {r['href']}" for r in results[:3]])
def calc_tool(expr):
try:
return str(eval(expr))
except Exception as e:
return f"Math error: {e}"
def run_tool(command):
with Sandbox(template="base") as sb:
result = sb.run(command)
return result.stdout or result.stderr or "No output."
tools = {
"search": search_tool,
"calc": calc_tool,
"run": run_tool
}
# Tool parser
def parse_tools(text):
for name in tools:
if f"<tool:{name}>" in text:
start = text.find(f"<tool:{name}>") + len(f"<tool:{name}>")
end = text.find(f"</tool:{name}>")
arg = text[start:end].strip()
return tools[name](arg)
return None
# Agent loop
def agent_chat(user_input, history=[]):
history.append({"role": "user", "content": user_input})
prompt = "\n".join([f"{m['role']}: {m['content']}" for m in history])
output = llm(prompt=prompt, stop=["user:", "system:"], echo=False)
response = output["choices"][0]["text"].strip()
result = parse_tools(response)
if result:
response += f"\n🔧 Tool Output:\n{result}"
history.append({"role": "assistant", "content": response})
return response
gr.ChatInterface(fn=agent_chat).launch()
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