Update app.py

app.py

@@ -1,3 +1,5 @@
+import requests
+import os
 import gradio as gr
 from datetime import datetime
 import json
@@ -5,6 +7,8 @@ import uuid
 
 APP_TITLE = "HumAI Midfielder Avatar"
 APP_VERSION = "v0.2.0-enterprise-demo"
+INFERENCE_URL = os.getenv("INFERENCE_URL", "")
+INFERENCE_API_KEY = os.getenv("INFERENCE_API_KEY", "")
 LIVE_PRODUCT_URL = "https://humai-orchestration-makerfire.vercel.app"
 BRAND_LAYER = "BPM RED Academy / MightHub HumAI Layer"
 PRODUCT_NAME = "HumAI Midfielder Avatar"
@@ -56,6 +60,68 @@ def normalize_selection(value, mapping, fallback):
 def clamp(value, min_value=0.52, max_value=0.96):
     return max(min_value, min(max_value, value))
 
+    def call_real_inference(prompt):
+
+    if not INFERENCE_URL:
+        return {
+            "success": False,
+            "fallback": True,
+            "content": "Inference endpoint not configured."
+        }
+
+    headers = {
+        "Authorization": f"Bearer {INFERENCE_API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "model": "FinC2E",
+        "messages": [
+            {
+                "role": "system",
+                "content": "You are FinC2E governance runtime."
+            },
+            {
+                "role": "user",
+                "content": prompt
+            }
+        ],
+        "temperature": 0.1,
+        "max_tokens": 400
+    }
+
+    try:
+
+        response = requests.post(
+            INFERENCE_URL,
+            headers=headers,
+            json=payload,
+            timeout=60
+        )
+
+        data = response.json()
+
+        content = (
+            data.get("choices", [{}])[0]
+            .get("message", {})
+            .get("content", "")
+        )
+
+        return {
+            "success": True,
+            "fallback": False,
+            "content": content,
+            "raw": data
+        }
+
+    except Exception as e:
+
+        return {
+            "success": False,
+            "fallback": True,
+            "content": str(e)
+        }
+
 
 def build_avatar_intro(domain_label, mode_label, scenario_label, priority):
     return (
@@ -72,6 +138,7 @@ def build_avatar_intro(domain_label, mode_label, scenario_label, priority):
 def evaluate_mission_control(domain, mode, scenario, priority, user_context):
     risk = "MEDIUM"
     score = 0.72
+    real_runtime = call_real_inference(user_context)
     recommendation = "Use structured Human-AI orchestration before taking operational action."
     explanation = (
         "HumAI structures the situation, evaluates domain context and prepares "
@@ -293,7 +360,9 @@ def evaluate_mission_control(domain, mode, scenario, priority, user_context):
         "doa_layer": DOA_FULL_NAME,
         "version": APP_VERSION,
         "execution_mode": "deterministic_enterprise_fallback",
-        "ai_assisted": False,
+        "ai_assisted": real_runtime["success"],
+        "inference_fallback": real_runtime["fallback"],
+        "runtime_output": real_runtime["content"],
         "domain": domain,
         "mode": mode,
         "scenario": scenario,