Fixed gradio loader

server.py

@@ -34,11 +34,8 @@ import urllib.error
 import urllib.parse
 import urllib.request
 from pathlib import Path
-from gradio import Server
+import gradio as gr
 from typing import Any, Dict, List, Optional
-from fastapi.responses import HTMLResponse
-from fastapi.staticfiles import StaticFiles
-from fastapi.middleware.cors import CORSMiddleware
 from dotenv import load_dotenv
 
 load_dotenv()
@@ -62,13 +59,6 @@ logging.basicConfig(
 )
 logger = logging.getLogger("dashboard-server")
 
-# ─────────────────────────────────────────────────────────────────────────────
-# gr.Server
-# ─────────────────────────────────────────────────────────────────────────────
-
-FRONTEND_DIR = BASE_DIR / "frontend"
-INDEX_HTML   = FRONTEND_DIR / "index.html"
-
 # ─────────────────────────────────────────────────────────────────────────────
 # Backend URL
 # ─────────────────────────────────────────────────────────────────────────────
@@ -166,60 +156,10 @@ def run_pipeline(
         "links_graph": links_graph,
     }
 
-server = Server(
-    title="Iroha Causal Terminal",
-    description=(
-        "Iroha Financial Intelligence — real-time causal probability matrix, "
-        "HHKD decomposition, DoFlow inference and sector hierarchy over NIFTY50."
-    ),
-    version="2.0.0",
-)
-
-# ── CORS (same as main.py) ────────────────────────────────────────────────
-server.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# ── Static files — mount frontend/ at /static ─────────────────────────────
-server.mount(
-    "/static",
-    StaticFiles(directory=str(FRONTEND_DIR)),
-    name="static",
-)
-
-# ─────────────────────────────────────────────────────────────────────────────
-# HTML route — serves the custom frontend
-# ─────────────────────────────────────────────────────────────────────────────
-
-@server.get("/", response_class=HTMLResponse, include_in_schema=False)
-async def serve_index():
-    """Serve the Iroha Causal Terminal SPA."""
-    if not INDEX_HTML.exists():
-        return HTMLResponse("<h1>Frontend not found. Run from backend/</h1>", status_code=500)
-    return HTMLResponse(INDEX_HTML.read_text(encoding="utf-8"))
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Health check
-# ─────────────────────────────────────────────────────────────────────────────
-
-@server.get("/v2/health", tags=["utility"])
-async def health():
-    """Lightweight health-check used by the frontend API banner."""
-    return {"status": "ok", "version": "2.0.0"}
-
-
 # ─────────────────────────────────────────────────────────────────────────────
-# gr.Server API endpoints (Gradio-backed — queue + SSE streaming)
-# These are reachable via the Gradio JS Client as well as plain fetch().
+# Helpers
 # ─────────────────────────────────────────────────────────────────────────────
 
-# ── Helpers ───────────────────────────────────────────────────────────────
-
 # URL of the noisy_boy_backend — used to fetch the validated causal matrix.
 # By default, point to ourselves since we now successfully mount the backend routers.
 # Override via BACKEND_API_URL env var if running a separate backend on 8000.
@@ -466,16 +406,6 @@ def _abduct_and_predict(
 #   3. Optionally consult pywhyllm guidance from the backend payload.
 
 
-@server.api(
-    name="run_inference",
-    description=(
-        "Run causal inference (association / intervention / counterfactual) "
-        "using a validated causal matrix fetched from the backend API. "
-        "Layers: 1=Association(DoWhy backdoor), 2=Intervention(SCM propagation), "
-        "3=Counterfactual(SCM abduction)."
-    ),
-    concurrency_limit=4,
-)
 def run_inference(
     ticker: str = "RELIANCE",
     mode: str = "assert",
@@ -898,21 +828,53 @@ def run_inference(
 
 
 # ─────────────────────────────────────────────────────────────────────────────
-# Entry point
+# Gradio UI & Entry point
 # ─────────────────────────────────────────────────────────────────────────────
 
+with gr.Blocks(title="Iroha Causal Terminal") as demo:
+    gr.Markdown("# Iroha Causal Terminal")
+    gr.Markdown("Iroha Financial Intelligence — real-time causal probability matrix, HHKD decomposition, DoFlow inference and sector hierarchy over NIFTY50.")
+    
+    with gr.Row():
+        ticker = gr.Textbox(label="Ticker", value="RELIANCE")
+        mode = gr.Dropdown(choices=["assert", "intervene", "counterfactual"], label="Mode", value="assert")
+        treatment = gr.Textbox(label="Treatment", value="Revenue")
+        outcome = gr.Textbox(label="Outcome", value="NetIncome")
+        target = gr.Textbox(label="Target", value="")
+        
+    with gr.Row():
+        value = gr.Number(label="Value", value=1.1)
+        cf_value = gr.Number(label="CF Value")
+        value_type = gr.Dropdown(choices=["absolute", "multiplier", "percent_change"], label="Value Type", value="multiplier")
+        horizon = gr.Number(label="Horizon", value=5, precision=0)
+        observed_t = gr.Number(label="Observed T", value=-1, precision=0)
+        threshold = gr.Number(label="Threshold", value=0.5)
+        
+    with gr.Row():
+        use_pywhyllm = gr.Checkbox(label="Use PyWhyLLM", value=False)
+        return_assumption_report = gr.Checkbox(label="Return Assumption Report", value=False)
+        
+    btn = gr.Button("Run Inference")
+    out = gr.JSON(label="Result")
+    
+    btn.click(
+        fn=run_inference,
+        inputs=[
+            ticker, mode, treatment, outcome, target, value, cf_value, value_type,
+            horizon, observed_t, threshold, use_pywhyllm, return_assumption_report
+        ],
+        outputs=out,
+        api_name="run_inference"
+    )
+
 if __name__ == "__main__":
     port = int(os.environ.get("GRADIO_SERVER_PORT", os.environ.get("PORT", "7860")))
     host = os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0")
 
     logger.info(f"Starting Iroha Causal Terminal on {host}:{port}")
-    logger.info(f"  → Frontend : http://localhost:{port}/")
-    logger.info(f"  → API docs : http://localhost:{port}/docs")
 
-    server.launch(
+    demo.launch(
         server_name=host,
         server_port=port,
-        allowed_paths=[str(FRONTEND_DIR)],
         show_error=True,
-        quiet=False,
     )