Add application file

app.py

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+
+# from langchain import OpenAI, SQLDatabase, SQLDatabaseChain
+from langchain.agents import create_sql_agent
+from langchain.agents.agent_toolkits import SQLDatabaseToolkit
+from langchain.sql_database import SQLDatabase
+from langchain.llms.openai import OpenAI
+from langchain.agents import AgentExecutor
+import openai
+import json
+import gradio as gr
+import boto3 
+
+openai.api_key = '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'
+db = SQLDatabase.from_uri("postgresql://edaiadmin:PWD4EDAIPG!@54.167.203.239:17685/bubble",schema='langchain_testing')
+llm = OpenAI(temperature=0, verbose=True)
+
+access_key = 'AKIAX7J347JABCJGWURY'
+secret_key = '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'
+region= 'us-east-1'
+# Create S3 client
+s3_client = boto3.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_key,region_name=region)
+
+
+toolkit = SQLDatabaseToolkit(db=db, llm=llm)
+agent_executor = create_sql_agent(
+    llm=OpenAI(temperature=0),
+    toolkit=toolkit,
+    verbose=True
+)
+
+def generate_response(question):
+    prompt_template = """
+     Keep in mind that any site, building, or property related question should be routed to the real estate portal. 
+     Any Local or city or city-sector incentives programs are asking about the local incentives program table. 
+     Any State incentives programs are asking about the state incentives program table.
+     When the user asks about a state incentive, don't query the local_incentives_catalog table.
+     When the user asks about a local/city incentive, don't query the local_incentives_catalog table.
+     If you can't find the answer, make sure to look up the program field in the local and state incentives catalogs.
+     If your final answer is "I don't know", then respond with "Please adjust your question and try asking again."
+     
+     """
+    chain_response = agent_executor.run(question + prompt_template)
+    # bucket_name = 'your-bucket-name'
+    # file_name = 'flagged_text.txt'
+    # s3_client.put_object(Body=input_text, Bucket=bucket_name, Key=file_name)
+    
+    return chain_response
+
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.inputs.Textbox(label='Enter your question:', default='What is the Neighborhood Credit Fund in New York City?'),
+    outputs=gr.outputs.Textbox(label="EDai Analyst's Response:")
+    )
+
+iface.launch(share=True)
+
+
+

requirements.txt

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+requests==2.27.1
+pandas==1.4.2
+psycopg2-binary==2.9.3
+boto3==1.21.32
+openai==0.27.7
+langchain==0.0.184
+gradio==3.32.0