Update recommendation_engine.py

recommendation_engine.py

@@ -1,69 +1,69 @@
-# recommendation_engine.py
-import requests
-from bs4 import BeautifulSoup
-import pandas as pd
-from sentence_transformers import SentenceTransformer, util
-import torch
-import numpy as np
-from langchain.callbacks.tracers import ConsoleCallbackHandler
-from langsmith import traceable
-
-model = SentenceTransformer("nomic-ai/nomic-embed-text-v1",trust_remote_code=True)
-
-catalog = pd.read_csv("data.csv")
-embeddings = torch.load("embeddings.pth")
-
-handler = ConsoleCallbackHandler() 
-
-def scrape_url(url):
-    try:
-        page = requests.get(url)
-        soup = BeautifulSoup(page.text, "html.parser")
-        return soup.get_text(separator=' ')
-    except Exception as e:
-        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, duration, jd_text=""):
-    cleaned_query = clean_query_text(query)
-    input_text = f"{cleaned_query}. Candidate should complete assessment in {duration} minutes. {jd_text}"
-    return input_text.strip()
-
-def get_recommendations(query_text, top_k=10,max_duration = None):
-    query_embedding = model.encode(query_text)
-    scores = util.cos_sim(query_embedding, embeddings)[0].numpy()
-    ranked_indices = np.argsort(-scores)
-
-    results = []
-    for idx in ranked_indices:
-        item = catalog.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
-
-@traceable(name="SHL Recommendation Trace")
-def traced_get_recommendations(query_text, top_k=10, max_duration=None):
-    return get_recommendations(query_text, top_k, max_duration)
+# recommendation_engine.py
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+import torch
+import numpy as np
+from langchain.callbacks.tracers import ConsoleCallbackHandler
+from langsmith import traceable
+
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1",trust_remote_code=True)
+
+catalog = pd.read_csv("data.csv")
+embeddings = torch.load("embeddings.pth",weights_only=False)
+
+handler = ConsoleCallbackHandler() 
+
+def scrape_url(url):
+    try:
+        page = requests.get(url)
+        soup = BeautifulSoup(page.text, "html.parser")
+        return soup.get_text(separator=' ')
+    except Exception as e:
+        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, duration, jd_text=""):
+    cleaned_query = clean_query_text(query)
+    input_text = f"{cleaned_query}. Candidate should complete assessment in {duration} minutes. {jd_text}"
+    return input_text.strip()
+
+def get_recommendations(query_text, top_k=10,max_duration = None):
+    query_embedding = model.encode(query_text)
+    scores = util.cos_sim(query_embedding, embeddings)[0].numpy()
+    ranked_indices = np.argsort(-scores)
+
+    results = []
+    for idx in ranked_indices:
+        item = catalog.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
+
+@traceable(name="SHL Recommendation Trace")
+def traced_get_recommendations(query_text, top_k=10, max_duration=None):
+    return get_recommendations(query_text, top_k, max_duration)