Data Interaction Application

User-Query-Portal-Superstore/Employee Details.json

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+[
+    {
+        "Name ":"Mr. Dinesh Sahu",
+        "Department ":"Sales Department",
+        "Support ":"Shipping and Delivery",
+        "Phone Number ":9685743214,
+        "Email Id ": "[email protected]"
+    },
+    {
+        "Name ":"Mrs. Nandini Anand",
+        "Department ":"Sales Department",
+        "Support ":"General Assistance",
+        "Phone Number ":9685743232,
+        "Email Id ": "[email protected]" 
+    },
+    {
+        "Name ":"Mr. Shaurya Shrivastava",
+        "Department ":"Technical Department",
+        "Support ":"Service Inquiry",
+        "Phone Number ":9685743233,
+        "Email Id ": "[email protected]"
+    },
+    {
+        "Name ":"Mr. Sandeep Singh",
+        "Department ":"Technical Department",
+        "Support ":"Technical Support",
+        "Phone Number ":9685743210,
+        "Email Id ": "[email protected]"
+    },
+    {
+        "Name ":"Mr. Raghunandan Verma",
+        "Department ":"Customer Service",
+        "Support ":"Information Request",
+        "Phone Number ":9685743250,
+        "Email Id ": "[email protected]"
+    },
+    {
+        "Name ":"Mr. Jayawardhan Rajagopal",
+        "Department ":"Customer Service",
+        "Support ":"Information Request",
+        "Phone Number ":9685743299,
+        "Email Id ": "[email protected]"
+    },
+    {
+        "Name ":"Mr. Sriman Narayana",
+        "Department ":"Sales Department",
+        "Support ":"Product Inquiry",
+        "Phone Number ":9685743299,
+        "Email Id ": "[email protected]" 
+    },
+    {
+        "Name ":"Mr. Jayaraj",
+        "Department ":"Technical Department",
+        "Support ":"Account Assistance",
+        "Phone Number ":9685743298,
+        "Email Id ": "[email protected]" 
+    }
+]

User-Query-Portal-Superstore/Intent Recognition.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import difflib
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from nltk.tokenize import RegexpTokenizer
+
+
+# In[2]:
+
+
+data = pd.read_excel('Jobs.xlsx')
+data.head()
+
+
+# In[3]:
+
+
+# removing punctuations from the query of the customer
+tokenizer = RegexpTokenizer(r'\w+')
+data['Query of the Customer'] = data['Query of the Customer'].apply(lambda x: ' '.join(tokenizer.tokenize(x)))
+
+
+# In[4]:
+
+
+# combining all the column's data into a single feature to use it for processing of the queries
+combine_feat = data['Query of the Customer'] + ' ' + data['Query Type'] + ' ' + data['Who Can Help']
+combine_feat
+
+
+# In[5]:
+
+
+# to vectorize the features
+
+vectorizer = TfidfVectorizer()
+feature_vectors = vectorizer.fit_transform(combine_feat)
+
+
+# In[6]:
+
+
+# similarity scores
+similarity  = cosine_similarity(feature_vectors)
+
+
+# In[7]:
+
+
+print(similarity)
+
+
+# In[8]:
+
+
+# taking user input as query
+query = input('Enter your query : ')
+
+
+# In[9]:
+
+
+# putting all the queries into one list
+list_of_all_queries =data['Query of the Customer'].tolist()
+list_of_all_queries
+
+
+# In[10]:
+
+
+# finding closest match to user input from the list of all queries
+
+find_close_match = difflib.get_close_matches(query, list_of_all_queries)
+find_close_match
+
+
+# In[11]:
+
+
+close_match = find_close_match[0]
+close_match
+
+
+# In[12]:
+
+
+#index of the closest match
+index_of_the_query = data[data['Query of the Customer'] == close_match]['index'].values[0]
+index_of_the_query
+
+
+# In[13]:
+
+
+# checking similarity with every query in the dataset
+
+similarity_score = list(enumerate(similarity[index_of_the_query]))
+similarity_score
+
+
+# In[14]:
+
+
+len(similarity_score)
+
+
+# In[15]:
+
+
+#sorting the similarity score list
+sorted_similarity_score = sorted(similarity_score, key = lambda x:x[1], reverse = True) 
+print(sorted_similarity_score)
+
+
+# In[16]:
+
+
+print('Your query is related to : \n')
+
+i = 1
+
+query_of_the_user = list()
+
+for query1 in sorted_similarity_score:
+  index = query1[0]
+  query_of_the_user.append(data[data.index==index]['Query Type'].values[0])
+
+print(query_of_the_user)
+
+# for name in query_type:
+#   print(name)
+
+
+# In[17]:
+
+
+print('Your query is related to : ')
+# Set to store unique elements encountered so far
+query_type = set()
+i = 0
+# Iterate over the list and print only those elements that are not in the set
+for word in query_of_the_user:
+    if word not in query_type:
+        print(word)
+        query_type.add(word)
+        i+=1
+
+        if i>=3:
+            break
+
+
+# ### Fetching the details from json file to help the user contact the necessary support
+
+# In[18]:
+
+
+import json
+
+def find_details(query_1):
+
+  # Load the JSON data from the file
+  with open("Employee Details.json", "r") as f:
+    data = json.load(f)
+
+  # Find employees with matching support
+  matching_details = []
+  for details in data:
+    if query_1.lower() in details["Support "].lower():
+      matching_details.append(details)
+
+  return matching_details
+
+
+# In[20]:
+
+
+query_1 = query_of_the_user[0]
+matching_details = find_details(query_1)
+
+print("USER's Query : ")
+print(query)
+print("Query related to : ",query_of_the_user[0])
+print("="*50)
+print("Please contanct the following person for help : ")
+print("="*50)
+if matching_details:
+  # Print employee information
+  for employee in matching_details:
+    print(f"Employee Name: {employee['Name ']}")
+    print(f"Department: {employee['Department ']}")
+    print(f"Support: {employee['Support ']}")
+    print(f"Phone Number: {employee['Phone Number ']}")
+    print(f"Email: {employee['Email Id ']}")
+    print("-" * 20)
+else:
+  print("No matching employees found.")
+
+
+# In[ ]:
+
+
+
+

User-Query-Portal-Superstore/README.md

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+Python application that simulates interaction with a database, processes user queries, and 
+returns relevant data.
+
+#### Link to the web app : 
+https://user-query-app-superstore-y8vraqdk2rbuepindrdkv8.streamlit.app/