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f5efb42 3527ca9 f5efb42 c8a3816 f5efb42 c8a3816 f5efb42 3527ca9 c8a3816 3527ca9 c8a3816 3527ca9 c8a3816 3527ca9 c8a3816 3527ca9 f5efb42 c8a3816 3527ca9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | from fastapi import FastAPI
import gradio as gr
from env import EmailTriageEnv
from app import smart_agent_logic
app = FastAPI()
# -------------------------
# REQUIRED API ENDPOINTS (DO NOT REMOVE)
# -------------------------
@app.post("/reset")
async def reset():
return {"status": "ok"}
@app.get("/status")
async def status():
return {"status": "online"}
# -------------------------
# GRADIO FUNCTION (THIS SHOWS REWARD + SCORE)
# -------------------------
def demo_fn(task):
env = EmailTriageEnv(task=task)
state = env.reset()
results = []
total_reward = 0.0
steps = 0
while True:
if state.get("done"):
break
desc = state["description"]
action = smart_agent_logic(desc)
state, reward, done, _, _ = env.step(action)
total_reward += reward
steps += 1
results.append(
f"### Step {steps}\n"
f"- 📧 Email: {desc}\n"
f"- 🤖 Action: {action}\n"
f"- ⭐ Reward: {reward:.2f}\n"
f"---\n"
)
if done:
break
score = total_reward / steps if steps > 0 else 0.0
return f"""
# 📊 Email Triage Results
{''.join(results)}
## 🏁 Final Score: **{score:.3f} / 1.000**
"""
# -------------------------
# GRADIO UI
# -------------------------
demo = gr.Interface(
fn=demo_fn,
inputs=gr.Dropdown(["easy", "medium", "hard"], label="Select Difficulty"),
outputs=gr.Markdown(label="Results"),
title="📧 Email Gatekeeper",
description="AI Agent for Email Triage using Reinforcement Learning"
)
# -------------------------
# MOUNT UI
# -------------------------
app = gr.mount_gradio_app(app, demo, path="/") |