csv qa code added

demo_app/main.py

@@ -1,26 +1,79 @@
 """Python file to serve as the frontend"""
+
+from langchain.agents import create_pandas_dataframe_agent
+from langchain.llms import OpenAI
+import pandas as pd
+import chainlit as cl
+import io
+
 import sys
 import os
 sys.path.append(os.path.abspath('.'))
 
-from langchain import PromptTemplate, OpenAI, LLMChain
-import chainlit as cl
-
 from chainlit import user_session
 
 user_env = user_session.get("env")
 
-# os.environ["OPENAI_API_KEY"] = ""
+# Chainlit fetches env variables from .env automatically
+
+""" from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+OPENAI_API_KEY= os.getenv("OPENAI_API_KEY")
+"""
+
+
+def create_agent(data: str, llm):
+    """Create a Pandas DataFrame agent."""
+    return create_pandas_dataframe_agent(llm, data, verbose=False)
+
 
-template = """Question: {question}
+@cl.on_chat_start
+async def on_chat_start():
 
-Answer: Let's think step by step."""
+    files = None
+
+    # Waits for user to upload csv data
+    while files == None:
+        files = await cl.AskFileMessage(
+            content="Please upload a csv file to begin!", accept=["text/csv"], max_size_mb= 100
+        ).send()
+
+    # load the csv data and store in user_session
+    file = files[0]
+    csv_file = io.BytesIO(file.content)
+    df = pd.read_csv(csv_file, encoding="utf-8")
+
+    # creating user session to store data
+    cl.user_session.set('data', df)
+
+    # Send response back to user
+    await cl.Message(
+        content=f"`{file.name}` uploaded! Now you ask me anything related to your data"
+    ).send()
+
+
+@cl.on_message
+async def main(message: str):
+
+    # Get data
+    df = cl.user_session.get('data')
 
-@cl.langchain_factory(use_async=True)
-def factory():
     user_env = cl.user_session.get("env")
     os.environ["OPENAI_API_KEY"] = user_env.get("OPENAI_API_KEY")
-    prompt = PromptTemplate(template=template, input_variables=["question"])
-    llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)
 
-    return llm_chain
+    # llm
+    llm = OpenAI()
+
+    # Agent creation
+    agent = create_agent(df, llm)
+
+    # Run model 
+    response = agent.run(message)
+
+    # Send a response back to the user
+    await cl.Message(
+        content=response,
+    ).send()