Update Climate_site/python_scripts/companies_function_own_details.py

Climate_site/python_scripts/companies_function_own_details.py

@@ -0,0 +1,77 @@
+##packages code
+
+import streamlit as st
+import pandas as pd
+import re
+import json
+from math import radians, cos, sin, asin, sqrt
+
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+
+path = 'Climate_site/python_scripts/'
+
+
+from sentence_transformers import SentenceTransformer, util
+
+@st.cache_resource
+def model_nlp():
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    return model
+
+
+@st.cache_data
+def load_dic():
+    f = open(path + "preqin_venturedealsdetails_encoded.json","r")
+    dic_companies = json.load(f)
+    return dic_companies
+    
+@st.cache_data
+def load_data():
+    url = path + "preqin_companies_IEA.tsv"
+    table = pd.read_csv(url, delimiter = "\t" , index_col = 0)
+    table = table.astype({'portfolio_company_id': 'str'})
+
+    return table
+
+
+
+
+table_companies = load_data()
+dic_companies = load_dic()
+
+
+
+
+
+def norm(vector):
+    return sqrt(sum(x * x for x in vector))    
+
+def cosine_similarity2(vec_a, vec_b):
+        norm_a = norm(vec_a)
+        norm_b = norm(vec_b)
+        dot = sum(a * b for a, b in zip(vec_a, vec_b))
+        return dot / (norm_a * norm_b)
+
+
+
+def get_similar_company(field, description , size):
+
+    model = model_nlp()
+    
+    companies = dic_companies[field]
+    
+    
+    encoded_description = model.encode(description)
+    scores = np.dot( np.array(list(companies.values())) , np.array(encoded_description))
+    
+    
+    dic_scores = { str(k) : { "score" : v }  for k , v in zip(companies.keys(), scores )}
+    
+    res = pd.DataFrame(dic_scores).T.sort_values("score" , ascending = False).head(size)
+    
+    return res.merge(table_companies , left_index = True , right_on = "portfolio_company_id" , how = "left")
+
+