Update app.py

app.py

@@ -1,33 +1,27 @@
 from fastapi import FastAPI, Request
 import gradio as gr
+import numpy as np
+from env import (
+    EmailTriageEnv, TASK_SPLITS,
+    URGENCY_LABELS, ROUTING_LABELS, RESOLUTION_LABELS,
+)
 
+# 1. FastAPI App Setup
 app = FastAPI()
 
-# --- Hackathon Grader ke liye ye endpoints zaroori hain ---
+# --- Hackathon Grader Endpoints ---
 @app.post("/reset")
 async def reset(request: Request):
-    # Yeh grader ko batayega ki env reset ho gaya
     return {"status": "success", "message": "Environment reset"}
 
 @app.post("/step")
 async def step(request: Request):
-    # Grader yahan se actions bhejega
     return {"status": "success"}
 
-# Gradio ko FastAPI ke andar mount kar do taaki dono chalein
-app = gr.mount_gradio_app(app, demo, path="/")
-
-import gradio as gr
-import numpy as np
-from env import (
-    EmailTriageEnv, TASK_SPLITS,
-    URGENCY_LABELS, ROUTING_LABELS, RESOLUTION_LABELS,
-)
-
+# 2. Logic Functions
 _LEGAL_SECURITY_KW   = {"lawsuit", "attorney", "sue", "ransomware", "extortion"}
 _BILLING_ESCALATE_KW = {"refund"}
 
-
 def _classify(email: dict) -> np.ndarray:
     kw      = set(email.get("keywords", []))
     context = email.get("context", "").lower()
@@ -44,56 +38,45 @@ def _classify(email: dict) -> np.ndarray:
         return np.array([0, 1, 1], dtype=np.int64)
     return np.array([0, 0, 0], dtype=np.int64)
 
-
 def run_task_demo(task: str) -> str:
     env = EmailTriageEnv(task=task, shuffle=False)
     env.reset(seed=42)
     email_queue = list(env._queue)
-
-    lines        = []
-    cumulative   = 0.0
-    terminated   = False
-    step         = 0
-
+    lines = []
+    cumulative = 0.0
+    terminated = False
+    step = 0
     while not terminated:
         email  = email_queue[step]
         action = _classify(email)
         _, norm_reward, terminated, _, info = env.step(action)
         cumulative += norm_reward
-
         raw = info["raw_reward"]
         ca  = info["correct_actions"]
-
         verdict = ("✅ EXACT" if raw >= 1.0 else
                    "🔶 PARTIAL" if raw > 0 else
                    "🚨 SECURITY MISS" if raw < 0 else "❌ WRONG")
-
         lines.append(
             f"#{step+1:02d} [{email['difficulty'].upper()}] "
             f"{email['description'][:40]}\n"
-            f"     Predicted : {URGENCY_LABELS[action[0]]} | "
+            f"      Predicted : {URGENCY_LABELS[action[0]]} | "
             f"{ROUTING_LABELS[action[1]]} | {RESOLUTION_LABELS[action[2]]}\n"
-            f"     Correct   : {URGENCY_LABELS[ca[0]]} | "
+            f"      Correct   : {URGENCY_LABELS[ca[0]]} | "
             f"{ROUTING_LABELS[ca[1]]} | {RESOLUTION_LABELS[ca[2]]}\n"
-            f"     Reward    : {raw:+.1f}  {verdict}\n"
+            f"      Reward    : {raw:+.1f}  {verdict}\n"
         )
         step += 1
-
     final = max(0.0, min(1.0, cumulative))
     lines.append(f"\n{'─'*50}")
     lines.append(f"Final Score : {final:.3f} / 1.0")
     return "\n".join(lines)
 
-
+# 3. Gradio Interface (Iska naam 'demo' hai)
 with gr.Blocks(title="Email Gatekeeper RL") as demo:
     gr.Markdown("""
 # 📧 Email Gatekeeper — RL Environment Demo
 **Meta x PyTorch Hackathon** | Gymnasium-based email triage agent
-
-The agent classifies each email across **3 simultaneous dimensions**:
-`Urgency` × `Department` × `Resolution Action`
 """)
-
     with gr.Row():
         task_dropdown = gr.Dropdown(
             choices=["easy", "medium", "hard"],
@@ -101,26 +84,17 @@ The agent classifies each email across **3 simultaneous dimensions**:
             label="Select Task",
         )
         run_btn = gr.Button("▶ Run Episode", variant="primary")
-
     output_box = gr.Textbox(
         label="Episode Results",
         lines=30,
         max_lines=50,
     )
-
     run_btn.click(fn=run_task_demo, inputs=task_dropdown, outputs=output_box)
 
-    gr.Markdown("""
-### Reward Function
-| Result | Reward |
-|---|---|
-| ✅ Exact Match (all 3 correct) | +1.0 |
-| 🔶 Partial (urgency correct, 1 wrong) | +0.2 |
-| 🔶 Partial (urgency correct, 2 wrong) | +0.1 |
-| 🚨 Security Miss | **-2.0** |
-| ❌ Wrong urgency | 0.0 |
-""")
-
+# 4. SABSE IMPORTANT: Mount Gradio to FastAPI (Yahan 'demo' exist karta hai)
+app = gr.mount_gradio_app(app, demo, path="/")
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    import uvicorn
+    # Hugging Face ke liye server_port 7860 zaroori hai
+    uvicorn.run(app, host="0.0.0.0", port=7860)