Update app.py

app.py

@@ -1,5 +1,59 @@
 import os
+from sqlalchemy import create_engine, inspect, URL
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_community.utilities import SQLDatabase
+from langchain_community.agent_toolkits import create_sql_agent
+from langchain import PromptTemplate, SQLDatabase, HuggingFaceHub
+from langchain_experimental.sql.base import SQLDatabaseChain
 import streamlit as st
+import pyodbc                                                                                           
 
+print(pyodbc.drivers()) 
 
-st.title("Generate responses from SQL tables")
+st.title("Generate responses from SQL tables")
+
+your_endpoint_url = "https://mht99ia9b939puzq.us-east-1.aws.endpoints.huggingface.cloud"
+
+template = """You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.
+            The final answer should be in a concise natural language. 
+            Use the following format:
+            Question: Question here
+            SQLQuery: SQL Query to run
+            SQLResult: Result of the SQLQuery
+            Answer: Final answer here.
+            Only use the following tables:
+            {table_info}
+            Question: {input}"""
+
+    
+if __name__ == '__main__':
+    connection_string = ("Driver=ODBC Driver 18 for SQL Server;Server=crawlersdb.c3pzpntwjvdf.us-east-1.rds.amazonaws.com;Database=SmartClever;PORT=1433;UID=CleverData;PWD={};TrustServerCertificate=yes;".format(os.environ['DB_PWD'])
+    )
+    connection_url = URL.create(
+        "mssql+pyodbc", 
+        query={"odbc_connect": connection_string}
+    )
+    engine = create_engine(connection_url)
+    db = SQLDatabase(engine=engine)
+
+    llm = HuggingFaceEndpoint(
+        endpoint_url=f"{your_endpoint_url}",
+        max_new_tokens=4096,
+        top_k=10,
+        top_p=0.95,
+        typical_p=0.95,
+        temperature=0.01,
+        repetition_penalty=1.03,
+    )
+
+    prompt = PromptTemplate(template=template, input_variables=["input","table_info","top_k"])
+    sql_chain = SQLDatabaseChain.from_llm(prompt=prompt, llm=llm, db=db, verbose=True)
+
+    question = st.text_input("Ask a question in natural language and press enter")
+
+    if question != "":
+        try:
+            response = sql_chain.invoke(question)
+            st.write("Answer to your question is - ", response['result'].split("\n")[0])
+        except:
+            st.write("Sorry I cannot answer that. Please try again later.")