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import os
import json
import pandas as pd
import gradio as gr
import openai
import time
from cpr import brd_g
from typing import List, Dict
from langchain.document_loaders import AirtableLoader
username = os.getenv('USERNAME')
password = os.getenv('PASSWORD')
# Set up API keys
AIRTABLE_API_KEY = os.getenv("AIRTABLE_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
BASE_ID = os.getenv("base_id")
TABLE_ID = os.getenv("table_id")
VIEW = os.getenv("view")
# Set up OpenAI client
openai.api_key = OPENAI_API_KEY
client = openai.Client(api_key=OPENAI_API_KEY)
# Set up assistant
ASSISTANT_ID = os.getenv('assistant_id')
assistant = client.beta.assistants.retrieve(ASSISTANT_ID)
thread = client.beta.threads.create()
# Function to load data from Airtable and return as a DataFrame
def load_airtable_data() -> pd.DataFrame:
loader = AirtableLoader(AIRTABLE_API_KEY, TABLE_ID, BASE_ID, view=VIEW)
documents = loader.load()
data = []
for doc in documents:
try:
record = json.loads(doc.page_content)
data.append(record)
except json.JSONDecodeError:
print(f"Warning: Could not parse JSON for document: {doc.page_content[:100]}...")
data.append({"raw_content": doc.page_content})
return pd.DataFrame(data)
# Load Airtable data into DataFrame
try:
airtable_data_df = load_airtable_data()
print(f"Successfully loaded data from Airtable.")
except Exception as e:
print(f"Error loading Airtable data: {str(e)}")
airtable_data_df = pd.DataFrame()
# Function to chat with the assistant
def chat_with_assistant(message: str, dataframe: pd.DataFrame) -> str:
dataframe_csv = dataframe.to_csv(index=False)
full_message = f"""
Here is the student information:
{dataframe_csv}
Here is my question: {message}
Only output your answer without code.
"""
client.beta.threads.messages.create(
thread_id=thread.id,
role="user",
content=full_message
)
run = client.beta.threads.runs.create(
thread_id=thread.id,
assistant_id=ASSISTANT_ID
)
while True:
run_status = client.beta.threads.runs.retrieve(thread_id=thread.id, run_id=run.id)
if run_status.status == 'completed':
messages = client.beta.threads.messages.list(thread_id=thread.id)
assistant_response = messages.data[0].content[0].text.value
break
time.sleep(1)
return assistant_response
# Define the Gradio interface
def gradio_interface(question: str) -> str:
return chat_with_assistant(question, airtable_data_df)
with gr.Blocks() as demo:
iface = gr.Interface(
fn=gradio_interface,
inputs="text",
outputs=gr.Markdown(),
title="📅 Summative Assessment Tracker",
description="I am here to help you schedule summative assessments for your students",
)
gr.Markdown(brd_g)
# Launch the Gradio app
demo.launch(auth=(username,password))
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