Update app.py

app.py

@@ -1,47 +1,46 @@
 import pandas as pd
 import gradio as gr
-from retriever import get_relevant_passages
-from reranker import rerank
+import json
 from fastapi import FastAPI, Request
 from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
 from gradio.routes import mount_gradio_app
-import json
 
-# Define FastAPI app
-app = FastAPI()
+from retriever import get_relevant_passages
+from reranker import rerank
 
-# Enable CORS for Spaces
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+# FastAPI app instance
+app = FastAPI()
 
-# Load and clean CSV
+# --- CSV Loading and Cleaning ---
 def clean_df(df):
     df = df.copy()
     second_col = df.iloc[:, 2].astype(str)
+
     if second_col.str.contains('http').any() or second_col.str.contains('www').any():
         df["url"] = second_col
     else:
         df["url"] = "https://www.shl.com" + second_col.str.replace(r'^(?!/)', '/', regex=True)
+
     df["remote_support"] = df.iloc[:, 3].map(lambda x: "Yes" if x == "T" else "No")
     df["adaptive_support"] = df.iloc[:, 4].map(lambda x: "Yes" if x == "T" else "No")
     df["test_type"] = df.iloc[:, 5].apply(lambda x: eval(x) if isinstance(x, str) else x)
     df["description"] = df.iloc[:, 6]
-    df["duration"] = pd.to_numeric(df.iloc[:, 9].astype(str).str.extract(r'(\d+)')[0], errors='coerce')
+
+    df["duration"] = pd.to_numeric(
+        df.iloc[:, 9].astype(str).str.extract(r'(\d+)')[0],
+        errors='coerce'
+    )
+
     return df[["url", "adaptive_support", "remote_support", "description", "duration", "test_type"]]
 
 try:
     df = pd.read_csv("assesments.csv", encoding='utf-8')
     df_clean = clean_df(df)
 except Exception as e:
-    print(f"Error loading CSV: {e}")
+    print(f"Error loading data: {e}")
     df_clean = pd.DataFrame(columns=["url", "adaptive_support", "remote_support", "description", "duration", "test_type"])
 
+# --- Utility ---
 def validate_and_fix_urls(candidates):
     for candidate in candidates:
         if not isinstance(candidate, dict):
@@ -57,6 +56,7 @@ def validate_and_fix_urls(candidates):
             candidate['url'] = f"https://www.shl.com{url}" if url.startswith('/') else f"https://www.shl.com/{url}"
     return candidates
 
+# --- Core Recommend Logic ---
 def recommend(query):
     if not query.strip():
         return {"error": "Please enter a job description"}
@@ -65,8 +65,8 @@ def recommend(query):
         if top_k_df.empty:
             return {"error": "No matching assessments found"}
         top_k_df['test_type'] = top_k_df['test_type'].apply(
-            lambda x: x if isinstance(x, list) else 
-                     (eval(x) if isinstance(x, str) and x.startswith('[') else [str(x)])
+            lambda x: x if isinstance(x, list) else
+            (eval(x) if isinstance(x, str) and x.startswith('[') else [str(x)])
         )
         top_k_df['duration'] = top_k_df['duration'].fillna(-1).astype(int)
         top_k_df.loc[top_k_df['duration'] == -1, 'duration'] = None
@@ -78,17 +78,32 @@ def recommend(query):
         return result
     except Exception as e:
         import traceback
-        return {"error": f"Error: {str(e)}", "trace": traceback.format_exc()}
+        print(traceback.format_exc())
+        return {"error": f"Error processing request: {str(e)}"}
 
-# --- API Endpoints ---
-@app.get("/health")
+# --- Gradio Interface ---
+iface = 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."
+)
+
+# --- Mount Gradio at "/" ---
+app = mount_gradio_app(app, iface, path="/")
+
+# --- /health Endpoint ---
+@app.get("/health", response_class=JSONResponse)
 async def health_check():
     return JSONResponse(
         status_code=200,
         content={"status": "healthy"},
         media_type="application/json"
     )
-@app.post("/recommend")
+
+# --- /recommend Endpoint ---
+@app.post("/recommend", response_class=JSONResponse)
 async def recommend_api(request: Request):
     body = await request.json()
     query = body.get("query", "").strip()
@@ -104,7 +119,3 @@ async def recommend_api(request: Request):
         content=result,
         media_type="application/json"
     )
-# Mount Gradio app at root
-app = mount_gradio_app(app, gradio_iface, path="/")
-
-# Hugging Face Spaces runs `app` object, so no need for __main__