Update app.py

app.py

@@ -1,25 +1,21 @@
 import pandas as pd
 import gradio as gr
 import json
-from fastapi import FastAPI, Request
+from fastapi import FastAPI
 from fastapi.responses import JSONResponse
-
 from retriever import get_relevant_passages
 from reranker import rerank
 
-# ✅ Top-level FastAPI app declaration
-app = FastAPI()
-
-# --- Load and clean CSV ---
+# Load and clean CSV
 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)
@@ -28,6 +24,7 @@ def clean_df(df):
 
     return df[["url", "adaptive_support", "remote_support", "description", "duration", "test_type"]]
 
+# Load data and clean
 try:
     df = pd.read_csv("assesments.csv", encoding='utf-8')
     df_clean = clean_df(df)
@@ -35,7 +32,7 @@ except Exception as e:
     print(f"Error loading data: {e}")
     df_clean = pd.DataFrame(columns=["url", "adaptive_support", "remote_support", "description", "duration", "test_type"])
 
-# --- Fix URLs ---
+# Fix URLs
 def validate_and_fix_urls(candidates):
     for candidate in candidates:
         if not isinstance(candidate, dict):
@@ -51,7 +48,7 @@ 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
 
-# --- Recommendation Logic ---
+# Recommendation Logic
 def recommend(query):
     if not query.strip():
         return {"error": "Please enter a job description"}
@@ -76,12 +73,24 @@ def recommend(query):
         print(traceback.format_exc())
         return {"error": f"Error processing request: {str(e)}"}
 
-# --- FastAPI Health Endpoint ---
+# Initialize Gradio App
+def gradio_interface(query):
+    return recommend(query)
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    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-like Health Endpoint in Gradio
 @app.get("/health")
 async def health():
     return JSONResponse(content={"status": "healthy"}, status_code=200)
 
-# --- FastAPI Recommendation API ---
+# FastAPI-like Recommendation Endpoint in Gradio
 @app.post("/recommend")
 async def recommend_api(request: Request):
     try:
@@ -94,19 +103,11 @@ async def recommend_api(request: Request):
     except Exception as e:
         return JSONResponse(content={"error": str(e)}, status_code=500)
 
-# --- Gradio UI --- (integrating Gradio directly with FastAPI)
-def gradio_interface(query):
-    return recommend(query)
-
-iface = gr.Interface(
-    fn=gradio_interface,
-    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."
-)
+# Use Gradio app with the FastAPI app
+app = FastAPI()
 
-# Make sure to launch Gradio on the root path for HuggingFace Spaces
 @app.on_event("startup")
 async def startup():
+    # Launch Gradio app when FastAPI app starts
     iface.launch(inline=True, server_name="0.0.0.0", server_port=7860)
+