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| import streamlit as st | |
| from transformers import OpenAIGPT | |
| import datetime | |
| import json | |
| from langchain_community.llms import OpenAI | |
| # Initialize OpenAI client | |
| client = OpenAIGPT(api_key=None) | |
| # Define function for interacting with GPT-3.5 | |
| def chat_gpt(prompt): | |
| response = client.chat_completions( | |
| model_name_or_path="openai/gpt-3.5-turbo", | |
| messages=[{"role": "user", "content": prompt}] | |
| ) | |
| return response.choices[0].message.strip() | |
| # Step 2: Data Preprocessing | |
| def parse_user_query(query): | |
| # Assuming default dates are in ISO 8601 format (YYYY-MM-DD) | |
| default_start_date = datetime.date.today() - datetime.timedelta(days=365) | |
| default_end_date = datetime.date.today() | |
| # Implement parsing logic here | |
| return { | |
| "entity": "Company", | |
| "parameter": "Metric", | |
| "start_date": default_start_date.isoformat(), | |
| "end_date": default_end_date.isoformat() | |
| } | |
| # Step 3: Interacting with the GPT-3.5 API | |
| def query_gpt(prompt): | |
| return chat_gpt(prompt) | |
| # Step 4: Generating JSON Output | |
| def generate_json_output(entity, parameter, start_date, end_date): | |
| # Parse start_date and end_date strings to datetime objects | |
| start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d') | |
| end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d') | |
| json_output = { | |
| "entity": entity, | |
| "parameter": parameter, | |
| "start_date": start_date.date().isoformat(), # Extract date part and convert to ISO format | |
| "end_date": end_date.date().isoformat() # Extract date part and convert to ISO format | |
| } | |
| return json_output | |
| # Step 5: Testing and Validation | |
| def test_application(): | |
| # Test user queries | |
| user_queries = [ | |
| "What is the revenue of Apple from 2023-01-01 to 2023-12-31?", | |
| "Show me the profit margin of Amazon for the last quarter.", | |
| "Compare the GMV of Alibaba and Walmart from 2022-01-01 to 2022-12-31." | |
| ] | |
| for query in user_queries: | |
| st.write("User Query:", query) | |
| parsed_query = parse_user_query(query) | |
| start_date, end_date = parsed_query["start_date"], parsed_query["end_date"] | |
| gpt_response = query_gpt(query) | |
| st.write("GPT-3.5 Response:", gpt_response) | |
| json_output = generate_json_output(parsed_query["entity"], parsed_query["parameter"], start_date, end_date) | |
| st.write("JSON Output:", json_output) | |
| st.write("-" * 50) | |
| # Initialize Streamlit app | |
| st.set_page_config(page_title="Company Performance Metrics Chatbot") | |
| st.header("Company Performance Metrics Chatbot") | |
| # Run the application | |
| test_application() | |