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import torch
import pandas as pd
import numpy as np
import gradio as gr
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import util, SentenceTransformer
import ast
import json
import re
# Load embeddings and data
embeddings = torch.load("embeddings.pth",weights_only = False) # shape: [377, 768]
data_df = pd.read_csv("data.csv")
# Load model once
# model = SentenceTransformer("all-MiniLM-L6-v2")
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1",trust_remote_code=True)
def extract_duration(text):
match = re.search(r"\d+", str(text)) # look for the first number
return int(match.group()) if match else 0
type_mapping = {
"A": "Ability & Aptitude",
"B": "Biodata & Situational Judgement",
"C": "Competencies",
"D": "Development & 360",
"E": "Assessment Exercises",
"K": "Knowledge & Skills",
"P": "Personality & Behavior",
"S": "Simulations"
}
def decode_test_types(test_type_raw):
try:
test_type_list = ast.literal_eval(test_type_raw)
return [type_mapping.get(code.strip(), code.strip()) for code in test_type_list]
except Exception:
return []
def clean_query_text(text):
replacements = {
"Java Script": "JavaScript",
"java script": "JavaScript",
"Java script": "JavaScript"
}
for wrong, correct in replacements.items():
text = text.replace(wrong, correct)
return text
def prepare_input(query):
cleaned_query = clean_query_text(query)
input_text = f"{cleaned_query}"
return input_text.strip()
def find_top_k(query: str, k: int = 5):
query_str = prepare_input(query)
query_vec = model.encode([query_str], normalize_embeddings=True)
scores = util.cos_sim(query_vec, embeddings)[0].numpy()
ranked_indices = np.argsort(-scores)
results = []
for idx in ranked_indices[:k]:
item = data_df.iloc[idx]
test_type_raw = item["test_types"]
test_type_decoded = decode_test_types(test_type_raw)
results.append({
"url": item["url"],
"adaptive_support": item["adaptive"],
"description": item["description"],
"duration": extract_duration(item["assessment_length"]),
"remote_support": item["remote"],
"test_type": test_type_decoded
})
# result = {
# "name": item["name"],
# "url": item["url"],
# "duration": item["assessment_length"],
# "remote": item["remote"],
# "adaptive": item["adaptive"]
# }
# results.append(result)
return results
def health():
return gr.JSON({"status": "healthy"})
def recommend(query):
recommended = find_top_k(query)
return gr.JSON({"recommended_assessments": recommended})
recommend_api = gr.Interface(fn=recommend, inputs=gr.Textbox(), outputs="json")
health_api = gr.Interface(fn=health, inputs=[], outputs="json")
# Gradio app with multiple endpoints
demo = gr.TabbedInterface(
interface_list=[recommend_api, health_api],
tab_names=["recommend", "health"]
)
if __name__ == "__main__":
demo.launch()
# Gradio Interface
# app = gr.Interface(
# fn=recommend,
# inputs=gr.Textbox(label="Job Description or Query"),
# outputs="json",
# examples=["Looking for java developer assessment", "Communication skills test"]
# )
# # Add `/health` route manually using FastAPI inside Gradio
# app.launch(server_name="0.0.0.0", server_port=7860, share=False, show_error=True, inline=False)
# with gr.Blocks() as demo:
# gr.Markdown("### SHL Assessment Recommender")
# query_input = gr.Textbox(label="Job Query", placeholder="e.g. JavaScript Developer")
# duration_input = gr.Textbox(label="Assessment Duration (minutes)", placeholder="e.g. 30")
# topk_input = gr.Slider(label="Top K Results", minimum=1, maximum=10, step=1, value=5)
# output = gr.JSON(label="Top Matches")
# submit_btn = gr.Button("Submit")
# def process(query, duration, top_k):
# return find_top_k(query, duration, top_k)
# submit_btn.click(fn=process, inputs=[query_input, duration_input, topk_input], outputs=[output])
# demo.launch()
# def find_top_k(query_json, k=5):
# query_str = prepare_input(query_json)
# # Convert query to vector
# query_vec = model.encode([query_str], normalize_embeddings=True)
# # Cosine similarity with precomputed normalized embeddings
# scores = util.cos_sim(query_vec, embeddings)[0].numpy()
# ranked_indices = np.argsort(-scores)
# results = []
# for idx in ranked_indices[:k]:
# item = data_df.iloc[idx]
# result = {
# "name": item["name"],
# "url": item["url"],
# "remote_testing": item["remote"],
# "adaptive": item["adaptive"],
# "duration": item["assessment_length"],
# "test_type": item["test_types"],
# }
# results.append(result)
# return results
# # Gradio Interface
# with gr.Blocks() as demo:
# gr.Markdown("### RAG Gradio Demo with JSON Query")
# json_input = gr.Textbox(label="JSON Query (as JSON string)")
# output = gr.JSON(label="Top Matches from Data")
# def process(json_input_str):
# try:
# query_json = json.loads(json_input_str)
# results = find_top_k(query_json)
# return results
# except Exception as e:
# return {"error": str(e)}
# submit_btn = gr.Button("Submit")
# submit_btn.click(fn=process, inputs=[json_input], outputs=[output])
# demo.launch()
# import torch
# import pandas as pd
# import numpy as np
# import gradio as gr
# from sklearn.metrics.pairwise import cosine_similarity
# from sentence_transformers import util,SentenceTransformer
# # Load embeddings and data
# embeddings = torch.load("embeddings.pth") # shape: [377, 768]
# data_df = pd.read_csv("data.csv")
# def clean_query_text(text):
# replacements = {
# "Java Script": "JavaScript",
# "java script": "JavaScript",
# "Java script": "JavaScript"
# }
# for wrong, correct in replacements.items():
# text = text.replace(wrong, correct)
# return text
# def prepare_input(data):
# cleaned_query = clean_query_text(data.query)
# input_text = f"{cleaned_query}. Candidate should complete assessment in {data.duration} minutes."
# return input_text.strip()
# def find_top_k(query_json, k=5):
# query_str = prepare_input(query_json)
# # Convert query to vector
# from sentence_transformers import SentenceTransformer
# model = SentenceTransformer("all-MiniLM-L6-v2")
# query_vec = model.encode([query_str], normalize_embeddings=True)
# scores = util.cos_sim(query_vec, embeddings)[0].numpy()
# ranked_indices = np.argsort(-scores)
# results = []
# for idx in ranked_indices:
# item = data_df.iloc[idx]
# print(f"Matched: {item['name']} with duration {item['assessment_length']}")
# result = {
# "name": item["name"],
# "url": item["url"],
# "remote_testing": item["remote"],
# "adaptive": item["adaptive"],
# "duration": item['assessment_length'],
# "test_type": item["test_types"],
# }
# results.append(result)
# if len(results) >= top_k:
# break
# return results
# # Compute similarity
# # similarities = cosine_similarity(query_vec, embeddings.numpy())[0]
# # top_indices = similarities.argsort()[-k:][::-1]
# # results = data_df.iloc[top_indices].to_dict(orient="records")
# # return results
# with gr.Blocks() as demo:
# gr.Markdown("### RAG Gradio Demo with JSON Query")
# json_input = gr.Textbox(label="JSON Query (as string)")
# output = gr.JSON(label="Top Matches from Data")
# def process(json_input_str):
# try:
# import json
# query_json = json.loads(json_input_str)
# results = find_top_k(query_json)
# return results
# except Exception as e:
# return {"error": str(e)}
# submit_btn = gr.Button("Submit")
# submit_btn.click(fn=process, inputs=[json_input], outputs=[output])
# demo.launch()
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