Add application file

Dockerfile

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+FROM public.ecr.aws/lambda/python:3.9
+
+COPY app.py ${LAMBDA_TASK_ROOT}
+COPY requirements.txt ./
+
+RUN pip install -r requirements.txt --target "${LAMBDA_TASK_ROOT}"
+RUN chmod -R o+rX .
+
+CMD ["app.lambda_handler"]

README.md

@@ -1,12 +1 @@
----
-title: Edai Gradio Langchain
-emoji: 🏢
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# edai_gradio_test

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 
+
+
+from langchain import OpenAI, AgentExecutor, Tool, load_tools, initialize_agent
+from langchain.agents import AgentType
+from langchain.agents.tools import SQLAgent
+from langchain.memory import ReadOnlySharedMemory
+
+
+postgres_connection_str = os.environ['POSTGRES_CONNECTION_STR']
+access_key = os.environ['AWS_ACCESS_KEY_ID']
+secret_key = os.environ['AWS_SECRET_ACCESS_KEY']
+openai_api_key = os.environ['OPENAI_API_KEY'] 
+region= 'us-east-1'
+
+db = SQLDatabase.from_uri(postgres_connection_str,schema='langchain_testing')
+s3_client = boto3.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_key,region_name=region)
+llm = OpenAI(temperature=0, verbose=True, openai_api_key=openai_api_key)
+
+# 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)
+
+# Load the language model and tools
+llm = OpenAI()
+tools = load_tools(["llama_index"], llm=llm)
+
+# Define the agent
+agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
+
+# Define the Gradio interface
+def chatbot(user_name):
+    # Define the initial message
+    message = f"Hi {user_name}, how may I help you?"
+    # Define the memory
+    memory = ReadOnlySharedMemory()
+    # Define the SQL agent
+    sql_agent = SQLAgent(database="my_database.db", table="my_table")
+    # Loop until the user ends the conversation
+    
+    while True:
+        # Get the user's input
+        user_input = gr.text_area(message, default_value="")
+        
+        # If the user ends the conversation, break the loop
+        if user_input.lower() in ["bye", "goodbye", "exit"]:
+            break
+        # Get the answer from the SQL agent
+        answer = sql_agent.run(user_input, memory=memory)
+        # Ask if the user needs anything else
+        message = f"{answer}\n\nDo you need anything else?"
+    # Return the final message
+    return "Goodbye!"
+
+# Define the Gradio interface
+iface = gr.Interface(fn=chatbot, inputs=["text"], outputs="text", title="LangChain Chatbot", description="A chatbot powered by LangChain and Gradio.")
+# Launch the interface
+iface.launch()

flagged/log.csv

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+Enter your question:,EDai Analyst's Response:,flag,username,timestamp
+What is the Neighborhood Credit Fund in New York City?,The Neighborhood Credit Fund provides competitive interest rate loans to small and emerging businesses in New York City.,,,2023-05-30 16:34:18.482490

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