Create app.py

app.py

@@ -0,0 +1,84 @@
+# app.py
+import asyncio
+import gradio as gr
+from typing import List, Tuple
+from agents import FileSearchTool, Agent, ModelSettings, TResponseInputItem, Runner, RunConfig, trace
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+VECTOR_STORE_ID = os.getenv("VECTOR_STORE_ID")
+WORKFLOW_ID = os.getenv("WORKFLOW_ID")
+
+# --- Tools & Agent setup ---
+file_search = FileSearchTool(
+    vector_store_ids=[VECTOR_STORE_ID]
+)
+
+my_agent = Agent(
+    name="My agent",
+    instructions=(
+        "Ante la pregunta del usuario, siempre busca en el file search la información "
+        "y utiliza la información recopilada para generar una respuesta."
+    ),
+    model="gpt-4.1-mini",
+    tools=[file_search],
+    model_settings=ModelSettings(
+        temperature=1,
+        top_p=1,
+        max_tokens=2048,
+        store=True
+    )
+)
+
+def _to_items_from_history(history: List[Tuple[str, str]]) -> List[TResponseInputItem]:
+    """Convert Gradio chat history to Agent format."""
+    items: List[TResponseInputItem] = []
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            items.append({
+                "role": "user",
+                "content": [{"type": "input_text", "text": user_msg}],
+            })
+        if assistant_msg:
+            items.append({
+                "role": "assistant",
+                "content": [{"type": "output_text", "text": assistant_msg}],
+            })
+    return items
+
+async def agent_reply(message: str, history: List[Tuple[str, str]]):
+    """Handler for Gradio ChatInterface."""
+    conversation_items = _to_items_from_history(history)
+    conversation_items.append({
+        "role": "user",
+        "content": [{"type": "input_text", "text": message}],
+    })
+
+    with trace("Gradio message"):
+        result = await Runner.run(
+            my_agent,
+            input=conversation_items,
+            run_config=RunConfig(trace_metadata={
+                "__trace_source__": "agent-builder",
+                "workflow_id": WORKFLOW_ID
+            })
+        )
+
+    return result.final_output_as(str)
+
+# --- Updated Gradio ChatInterface ---
+demo = gr.ChatInterface(
+    fn=agent_reply,
+    title="My agent (FileSearch-powered)",
+    description=(
+        "Este chat usa un agente con FileSearchTool. "
+        "Escribe tu pregunta y el agente buscará primero en el file search antes de responder."
+    ),
+    # only valid arguments for Gradio 4.x
+    examples=["¿Qué información hay sobre el proyecto X?", "Resume el documento más reciente."],
+    theme="soft",
+    submit_btn="Enviar",
+    clear_btn="Limpiar",
+)