Update app.py
Browse files
app.py
CHANGED
@@ -7,6 +7,9 @@ import gradio as gr
|
|
7 |
import numpy as np
|
8 |
import time
|
9 |
import os
|
|
|
|
|
|
|
10 |
#import pkg_resources
|
11 |
|
12 |
'''
|
@@ -18,6 +21,38 @@ for package, version in installed_packages.items():
|
|
18 |
print(f"{package}=={version}")
|
19 |
'''
|
20 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
21 |
# Load the chatbot model
|
22 |
chatbot_model_name = "microsoft/DialoGPT-medium"
|
23 |
tokenizer = AutoTokenizer.from_pretrained(chatbot_model_name)
|
@@ -28,19 +63,13 @@ sql_model_name = "microsoft/tapex-large-finetuned-wtq"
|
|
28 |
sql_tokenizer = TapexTokenizer.from_pretrained(sql_model_name)
|
29 |
sql_model = BartForConditionalGeneration.from_pretrained(sql_model_name)
|
30 |
|
31 |
-
|
32 |
-
"year": [1896, 1900, 1904, 2004, 2008, 2012],
|
33 |
-
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
|
34 |
-
}
|
35 |
-
table = pd.DataFrame.from_dict(data)
|
36 |
-
|
37 |
-
sql_response = None
|
38 |
|
39 |
def predict(input, history=[]):
|
40 |
|
41 |
-
global sql_response
|
42 |
# Check if the user input is a question
|
43 |
-
is_question = "?" in input
|
44 |
|
45 |
'''
|
46 |
if is_question:
|
|
|
7 |
import numpy as np
|
8 |
import time
|
9 |
import os
|
10 |
+
|
11 |
+
import pyodbc
|
12 |
+
|
13 |
#import pkg_resources
|
14 |
|
15 |
'''
|
|
|
21 |
print(f"{package}=={version}")
|
22 |
'''
|
23 |
|
24 |
+
'''
|
25 |
+
# Replace the connection parameters with your SQL Server information
|
26 |
+
server = 'your_server'
|
27 |
+
database = 'your_database'
|
28 |
+
username = 'your_username'
|
29 |
+
password = 'your_password'
|
30 |
+
driver = 'SQL Server' # This depends on the ODBC driver installed on your system
|
31 |
+
|
32 |
+
# Create the connection string
|
33 |
+
connection_string = f'DRIVER={{{driver}}};SERVER={server};DATABASE={database};UID={username};PWD={password}'
|
34 |
+
|
35 |
+
# Connect to the SQL Server
|
36 |
+
conn = pyodbc.connect(connection_string)
|
37 |
+
|
38 |
+
#============================================================================
|
39 |
+
# Replace "your_query" with your SQL query to fetch data from the database
|
40 |
+
query = 'SELECT * FROM your_table_name'
|
41 |
+
|
42 |
+
# Use pandas to read data from the SQL Server and store it in a DataFrame
|
43 |
+
df = pd.read_sql_query(query, conn)
|
44 |
+
|
45 |
+
# Close the SQL connection
|
46 |
+
conn.close()
|
47 |
+
'''
|
48 |
+
|
49 |
+
data = {
|
50 |
+
"year": [1896, 1900, 1904, 2004, 2008, 2012],
|
51 |
+
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
|
52 |
+
}
|
53 |
+
table = pd.DataFrame.from_dict(data)
|
54 |
+
|
55 |
+
|
56 |
# Load the chatbot model
|
57 |
chatbot_model_name = "microsoft/DialoGPT-medium"
|
58 |
tokenizer = AutoTokenizer.from_pretrained(chatbot_model_name)
|
|
|
63 |
sql_tokenizer = TapexTokenizer.from_pretrained(sql_model_name)
|
64 |
sql_model = BartForConditionalGeneration.from_pretrained(sql_model_name)
|
65 |
|
66 |
+
#sql_response = None
|
|
|
|
|
|
|
|
|
|
|
|
|
67 |
|
68 |
def predict(input, history=[]):
|
69 |
|
70 |
+
#global sql_response
|
71 |
# Check if the user input is a question
|
72 |
+
#is_question = "?" in input
|
73 |
|
74 |
'''
|
75 |
if is_question:
|