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Update app.py
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app.py
CHANGED
@@ -1,38 +1,30 @@
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import subprocess
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import sys
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# Install openai if it is not already installed
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subprocess.check_call([sys.executable, "-m", "pip", "install", "openai"])
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# Install langchain_community if it is not already installed
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subprocess.check_call([sys.executable, "-m", "pip", "install", "langchain_community"])
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# Install sentence-transformers if it is not already installed
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subprocess.check_call([sys.executable, "-m", "pip", "install", "sentence-transformers"])
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# Install sentence-transformers if it is not already installed
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subprocess.check_call([sys.executable, "-m", "pip", "install", "chromadb"])
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subprocess.check_call([sys.executable, "-m", "pip", "install", "huggingface_hub"])
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from huggingface_hub import login
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login("RAG")
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#huggingface-cli login
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import openai
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import os
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import uuid
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import json
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import gradio as gr
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#from openai import OpenAI
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from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
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from langchain_community.vectorstores import Chroma
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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#
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#
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client = openai
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# Set up embeddings and vectorstore
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search_kwargs={'k': 5}
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)
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#
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log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
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log_folder = log_file.parent
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@@ -64,26 +56,19 @@ scheduler = CommitScheduler(
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# Define the Q&A system message
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qna_system_message = """
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You are an AI assistant
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Answer only using the context provided.
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Do not mention anything about the context in your final answer.
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If the answer is not found in the context, it is very important for you to respond with "I don't know."
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Always quote the source when you use the context. Cite the relevant source at the end of your response under the section - Source:
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Do not make up sources. Use the links provided in the sources section of the context and nothing else. You are prohibited from providing other links/sources.
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"""
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qna_user_message_template = """
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###Context
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Here are some documents that are relevant to the question.
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{context}
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{question}
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css
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Copy code
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"""
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# Define the predict function
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context_for_query = ".".join(context_list)
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# Create messages
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prompt = [
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# Get response from the LLM
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try:
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messages=prompt,
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temperature=0
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)
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prediction = response.choices[0].message.content
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except Exception as e:
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prediction = str(e)
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# Log inputs and outputs to a local log file
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with scheduler.lock:
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with log_file.open("a") as f:
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f.write(json.dumps(
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}
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))
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f.write("\n")
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return prediction
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def get_predict(question, company):
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# Implement your prediction logic here
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company_map = {
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"AWS": "aws",
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"IBM": "IBM",
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"Meta": "meta",
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"Microsoft": "msft"
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}
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selected_company = company_map.get(company)
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if not selected_company:
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return "Invalid company selected"
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return predict(question, selected_company)
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# Set
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with gr.Blocks(theme="gradio/seafoam@>=0.0.1,<0.1.0") as demo:
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with gr.Row():
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company = gr.Radio(["AWS", "IBM", "Google", "Meta", "Microsoft"], label="Select a company")
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)
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demo.queue()
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demo.launch()
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import subprocess
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import sys
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import os
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import uuid
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import json
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from pathlib import Path
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from dotenv import load_dotenv
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from huggingface_hub import login, CommitScheduler
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import gradio as gr
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from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
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from langchain_community.vectorstores import Chroma
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import openai
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# Install required libraries if not already installed
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subprocess.check_call([sys.executable, "-m", "pip", "install", "openai", "langchain_community", "sentence-transformers", "chromadb", "huggingface_hub", "python-dotenv"])
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# Load environment variables from .env file
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load_dotenv()
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# Login to Hugging Face using token from environment variables
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hf_token = os.getenv("HF_TOKEN")
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if not hf_token:
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raise ValueError("Hugging Face token not found in environment variables. Set HF_TOKEN in your .env file.")
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login(hf_token)
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# Set OpenAI API key from environment variables
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openai.api_key = os.getenv("OPENAI_API_KEY") # Ensure OPENAI_API_KEY is in your .env file
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client = openai
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# Set up embeddings and vectorstore
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search_kwargs={'k': 5}
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)
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# Define logging configuration
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log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
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log_folder = log_file.parent
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# Define the Q&A system message
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qna_system_message = """
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You are an AI assistant helping Finsights Grey Inc., a financial technology firm, develop a Retrieval-Augmented Generation (RAG) system to automate extraction, summarization, and analysis of 10-K reports.
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Your knowledge base was last updated in August 2023.
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User questions will start with the token: ###Question.
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Answer only based on the provided context.
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If the answer is not found in the context, respond with "I don't know."
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"""
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qna_user_message_template = """
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###Context
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Here are some documents that are relevant to the question.
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{context}
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###Question
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{question}
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"""
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# Define the predict function
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context_for_query = ".".join(context_list)
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# Create messages
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prompt = [
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{'role': 'system', 'content': qna_system_message},
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{'role': 'user', 'content': qna_user_message_template.format(context=context_for_query, question=user_input)}
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]
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# Get response from the LLM
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try:
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messages=prompt,
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temperature=0
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)
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prediction = response.choices[0].message.content
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except Exception as e:
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prediction = str(e)
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# Log inputs and outputs to a local log file
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with scheduler.lock:
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with log_file.open("a") as f:
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f.write(json.dumps({
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'user_input': user_input,
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'retrieved_context': context_for_query,
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'model_response': prediction
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}))
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f.write("\n")
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return prediction
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# Define the prediction interface function
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def get_predict(question, company):
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company_map = {
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"AWS": "aws",
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"IBM": "IBM",
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"Meta": "meta",
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"Microsoft": "msft"
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}
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selected_company = company_map.get(company)
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if not selected_company:
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return "Invalid company selected"
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return predict(question, selected_company)
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# Set up the Gradio UI
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with gr.Blocks(theme="gradio/seafoam@>=0.0.1,<0.1.0") as demo:
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with gr.Row():
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company = gr.Radio(["AWS", "IBM", "Google", "Meta", "Microsoft"], label="Select a company")
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)
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demo.queue()
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demo.launch()
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