demo.py

import pandas as pd
import pprint
import google.generativeai as palm
import pandas as pd

import os

from square.client import Client
import gradio as gr
import io

from langchain.agents import initialize_agent, AgentType
from langchain.llms import GooglePalm
import pandas as pd
#from yolopandas import pd
from langchain.chains.question_answering import load_qa_chain
from langchain import PromptTemplate, LLMChain
from langchain.agents import create_pandas_dataframe_agent
from langchain.tools import DuckDuckGoSearchRun
from pandasai import PandasAI


from dotenv import load_dotenv

load_dotenv()


client = Client(
    access_token=os.environ['SQUARE_ACCESS_TOKEN'],
    environment='sandbox')

location_id='LFA70NPRQAEV1'

palm.configure(api_key=os.environ['PALM'])

models = [m for m in palm.list_models(
) if 'generateText' in m.supported_generation_methods]
model = models[0].name
print(model)


load_dotenv()



# Define a function to fetch inventory items

def fetch_inventory_items():
    response = client.inventory.batch_retrieve_inventory_counts(
        body={
            'catalog_object_ids': [],
            'location_ids': []
        }
    )
    print(response.status_code)
    print(response.body)
    inventory_items = response.body['counts']

    items_data = []
    for item in inventory_items:
        item_data = {
            'Item Name': item['catalog_object_id'],
            # SKU not available in response, using catalog_object_id as a placeholder
            'SKU': item['catalog_object_id'],
            'Quantity': item['quantity'],
            'Location ID': item['location_id']
        }
        items_data.append(item_data)

    return items_data


# Fetch inventory items
inventory_items = fetch_inventory_items()

# Convert inventory data to a Pandas dataframe
df = pd.DataFrame(inventory_items)


# Print the dataframe
print(df)
df.to_csv("data.csv", index=False)
# Retrieve catalog objects
catalog_objects = client.catalog.batch_retrieve_catalog_objects(
    body={
        'object_ids': df['Item Name'].tolist(),
        'include_related_objects': True
    }
)
catalog_objects = catalog_objects.body['objects']

#print(catalog_objects)
#llm = GooglePalm(google_api_key=os.environ['PALM'])
#llm.temperature = 0.1

#prompts = ['Explain the difference between effective and affective with examples']
#llm_result = llm._generate(prompts)

#print(llm_result.generations[0][0].text)





def questiondocument(user_question):
    load_dotenv()
    #df = pd.read_csv("data.csv")
    df = pd.read_excel("tour_op.xlsx")

    agent = create_pandas_dataframe_agent(GooglePalm(temperature=0, google_api_key=os.environ['PALM']), df, agent="structured_chat-zero-shot-react-description", verbose=True)
    response = agent.run(user_question)
    #response = pandas_ai(df, prompt=user_question)

    return response


demo = gr.Interface(

    fn=questiondocument,
    inputs=["text"],
    outputs=["text"],
    title="Ask Busy Helper",
)
demo.launch(share=True)