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7d9e4d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | import pandas as pd
import requests
import time
import json
API_URL = "http://localhost:8001/recommend"
FILE_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "Gen_AI Dataset.xlsx")
OUTPUT_CSV = os.path.join(os.path.dirname(os.path.dirname(__file__)), "submission.csv")
def get_recommendations(query, url=None):
payload = {"query": query}
if url:
payload["url"] = url
try:
response = requests.post(API_URL, json=payload)
response.raise_for_status()
return response.json()
except Exception as e:
print(f"Error calling API: {e}")
return []
import sys
import os
sys.path.append(os.path.join(os.path.dirname(os.path.dirname(__file__)), "shl_recommender", "src"))
from metrics import calculate_recall_at_k
def evaluate_recall():
print("Loading Train-Set...")
gt_df = pd.read_excel(FILE_PATH, sheet_name='Train-Set')
# Generate Predictions for Train Set
print("Generating predictions for Train Set...")
queries = gt_df['Query'].unique()
pred_data = []
for query in queries:
preds = get_recommendations(query)
for p in preds:
pred_data.append({
"Query": query,
"Assessment_url": p['url']
})
pred_df = pd.DataFrame(pred_data)
# Calculate Metrics
calculate_recall_at_k(pred_df, gt_df, k=10)
def generate_predictions():
print("\nGenerating predictions for Test-Set...")
df = pd.read_excel(FILE_PATH, sheet_name='Test-Set')
results = []
for idx, row in df.iterrows():
query = row['Query']
print(f"Processing Test Query {idx+1}/{len(df)}...")
preds = get_recommendations(query)
# Format for submission: Query, Assessment_url
# We need to list recommendations for each query
# The format example in assignment says:
# Query 1 Recommendation 1
# Query 1 Recommendation 2
for p in preds:
results.append({
"Query": query,
"Assessment_url": p['url']
})
# Save to CSV
res_df = pd.DataFrame(results)
res_df.to_csv(OUTPUT_CSV, index=False)
print(f"Predictions saved to {OUTPUT_CSV}")
if __name__ == "__main__":
# Ensure API is running
try:
requests.get("http://localhost:8001/health")
except:
print("API is not running! Please start src/app.py first.")
exit(1)
evaluate_recall()
generate_predictions()
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