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app.py

@@ -11068,15 +11068,9 @@ examples = [
     ["Logical reasoning and problem solving test"],
 ]
 
-# Build the Gradio interface
+# Build the Gradio interface - Gradio 6.0 compatible
 with gr.Blocks(
-    title="SHL Assessment Recommendation Engine",
-    theme=gr.themes.Soft(
-        primary_hue="blue",
-        secondary_hue="purple",
-        neutral_hue="slate",
-    ),
-    css=custom_css
+    title="SHL Assessment Recommendation Engine"
 ) as demo:
     
     # Header
@@ -11155,40 +11149,26 @@ with gr.Blocks(
 
 
 # ============================================================================
-# FASTAPI INTEGRATION FOR API ENDPOINT
+# FASTAPI/GRADIO INTEGRATION FOR API ENDPOINTS
 # ============================================================================
 
-from fastapi import FastAPI, Query
-from fastapi.middleware.cors import CORSMiddleware
-
-app = FastAPI(
-    title="SHL Assessment Recommendation API",
-    description="AI-powered assessment recommendations using 2-Stage RAG Pipeline",
-    version="2.0.0"
-)
+# Import FastAPI requirements
+from fastapi import Query as FastAPIQuery
+from pydantic import BaseModel
 
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+class RecommendRequest(BaseModel):
+    query: str
 
-@app.get("/health")
+# Add API routes to Gradio's built-in FastAPI app
+@demo.app.get("/health")
 def health_check():
     """Health check endpoint - returns exactly {status: healthy}"""
     return {"status": "healthy"}
 
-from pydantic import BaseModel
-
-class RecommendRequest(BaseModel):
-    query: str
-
-@app.get("/recommend")
+@demo.app.get("/recommend")
 def recommend_api_get(
-    query: str = Query(..., min_length=3, description="Job description or requirements"),
-    max_results: int = Query(default=10, ge=1, le=10, description="Max results")
+    query: str = FastAPIQuery(..., min_length=3, description="Job description or requirements"),
+    max_results: int = FastAPIQuery(default=10, ge=1, le=10, description="Max results")
 ):
     """Get assessment recommendations as JSON (GET method)"""
     results = engine.recommend(query.strip(), max_results=max_results)
@@ -11196,7 +11176,7 @@ def recommend_api_get(
         "recommended_assessments": results
     }
 
-@app.post("/recommend")
+@demo.app.post("/recommend")
 def recommend_api_post(request: RecommendRequest):
     """Get assessment recommendations as JSON (POST method)"""
     results = engine.recommend(request.query.strip(), max_results=10)
@@ -11204,13 +11184,12 @@ def recommend_api_post(request: RecommendRequest):
         "recommended_assessments": results
     }
 
-# Mount Gradio app to FastAPI
-app = gr.mount_gradio_app(app, demo, path="/")
-
 # ============================================================================
 # LAUNCH APPLICATION
 # ============================================================================
 
 if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+else:
+    # For HuggingFace Spaces - just expose the app
+    app = demo.app