Upload 6 files

.gitattributes

@@ -1,35 +1,2 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+nomic_model/* filter=lfs diff=lfs merge=lfs -text
+embeddings.pth filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1 @@
----
-title: RAG API
-emoji: 🏆
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.24.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+"# SHL Assessment Recommender" 

app.py

@@ -0,0 +1,262 @@
+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")  # 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()

embeddings.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b17237d1f2eb8b8fa8765c2dd87f8b18ed27ef4844067fb9898ce330bd8e5f5
+size 1732204

requirements.txt

@@ -0,0 +1,11 @@
+pandas
+numpy
+sentence-transformers
+torch
+requests
+transformers
+einops
+gradio
+scikit-learn
+
+