Update app.py

app.py

@@ -2,11 +2,21 @@ import pandas as pd
 import gradio as gr
 from fastapi import FastAPI, Request
 from fastapi.responses import JSONResponse, RedirectResponse
+from fastapi.middleware.cors import CORSMiddleware
 from retriever import get_relevant_passages
 from reranker import rerank
 
-# === FastAPI App ===
-app = FastAPI()
+# === Create FastAPI App ===
+app = FastAPI(title="SHL Assessment Recommender API")
+
+# Add CORS middleware to allow cross-origin requests
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
 # === Load and Clean CSV ===
 def clean_df(df):
@@ -28,6 +38,7 @@ def clean_df(df):
 try:
     df = pd.read_csv("assesments.csv", encoding='utf-8')
     df_clean = clean_df(df)
+    print(f"Successfully loaded {len(df_clean)} assessments")
 except Exception as e:
     print(f"Error loading data: {e}")
     df_clean = pd.DataFrame(columns=["url", "adaptive_support", "remote_support", "description", "duration", "test_type"])
@@ -50,7 +61,7 @@ def validate_and_fix_urls(candidates):
 
 # === Recommendation Logic ===
 def recommend(query):
-    if not query.strip():
+    if not query or not query.strip():
         return {"error": "Please enter a job description"}
     try:
         top_k_df = get_relevant_passages(query, df_clean, top_k=20)
@@ -73,15 +84,6 @@ def recommend(query):
         print(traceback.format_exc())
         return {"error": f"Error processing request: {str(e)}"}
 
-# === Gradio UI ===
-gr_interface = gr.Interface(
-    fn=recommend,
-    inputs=gr.Textbox(label="Enter Job Description", lines=4),
-    outputs="json",
-    title="SHL Assessment Recommender",
-    description="Paste a job description to get the most relevant SHL assessments."
-)
-
 # === FastAPI Endpoints ===
 @app.get("/health")
 async def health():
@@ -103,6 +105,24 @@ async def recommend_api(request: Request):
 async def root():
     return RedirectResponse(url="/gradio")
 
+# === Create Gradio Interface ===
+def create_gr_interface():
+    return gr.Interface(
+        fn=recommend,
+        inputs=gr.Textbox(label="Enter Job Description", lines=4, placeholder="Paste a job description here..."),
+        outputs=gr.JSON(),
+        title="SHL Assessment Recommender",
+        description="Paste a job description to get the most relevant SHL assessments.",
+        theme=gr.themes.Soft(),
+        allow_flagging="never"
+    )
+
 # === Mount Gradio App ===
-from gradio.routes import mount_gradio_app
-app = mount_gradio_app(app, gr_interface, path="/gradio")
+# This is the new recommended way to integrate Gradio with FastAPI
+gr_app = create_gr_interface()
+app = gr.mount_gradio_app(app, gr_app, path="/gradio")
+
+# Entry point for running the application directly
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)