Create agent.py

agent.py

@@ -0,0 +1,49 @@
+from llama_cpp import Llama
+from duckduckgo_search import DDGS
+import gradio as gr
+
+# Load model
+llm = Llama(model_path="models/Sam-reason-A1.Q4_K_S.gguf", n_ctx=2048)
+
+# Define tools
+def search_tool(query):
+    with DDGS() as ddgs:
+        results = ddgs.text(query)
+        return "\n".join([r["title"] + ": " + r["href"] for r in results[:3]])
+
+def calc_tool(expr):
+    try: return str(eval(expr))
+    except: return "Error in expression."
+
+tools = {
+    "search": search_tool,
+    "calc": calc_tool
+}
+
+# Tool registry parser
+def parse_tools(output):
+    for name in tools:
+        if f"<tool:{name}>" in output:
+            arg_start = output.find(f"<tool:{name}>") + len(f"<tool:{name}>")
+            arg_end = output.find(f"</tool:{name}>")
+            arg = output[arg_start:arg_end].strip()
+            result = tools[name](arg)
+            return f"Tool [{name}] → {result}"
+    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()
+
+    tool_result = parse_tools(response)
+    if tool_result:
+        response += f"\n{tool_result}"
+
+    history.append({"role": "assistant", "content": response})
+    return response
+
+gr.ChatInterface(fn=agent_chat).launch()