Delete app.py

app.py

@@ -1,165 +0,0 @@
-## Setup
-# Import the necessary Libraries
-import os
-import uuid
-import json
-import gradio as gr
-from dotenv import load_dotenv
-
-
-
-from openai import OpenAI
-from langchain_community.embeddings.sentence_transformer import (SentenceTransformerEmbeddings)
-from langchain_community.vectorstores import Chroma
-
-from huggingface_hub import CommitScheduler
-from pathlib import Path
-
-
-# Create Client
-load_dotenv()
-
-os.environ("ANYSCALE_API_KEY") = os.getenv("ANYSCALE_API_KEY")
-client = OpenAI(
-    base_url="https://api.endpoints.anyscale.com/v1",
-    api_key=os.environ("ANYSCALE_API_KEY")
-)
-
-
-# Define the embedding model and the vectorstore
-embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
-
-# Load the persisted vectorDB
-
-collection_name = 'finsights-grey-10k-2023'
-
-vectorstore_persisted = Chroma(
-    collection_name=collection_name,
-    embedding_function=embedding_model,
-    persist_directory='/content/finsights23_db'
-)
-
-retriever = vectorstore_persisted.as_retriever(
-    search_type="similarity",
-    search_kwargs={'k': 5},
-)
-
-# Prepare the logging functionality
-
-log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
-log_folder = log_file.parent
-
-scheduler = CommitScheduler(
-    repo_id="finsights-qna-logs",
-    repo_type="dataset",
-    folder_path=log_folder,
-    path_in_repo="data",
-    every=2
-)
-
-# Define the Q&A system message
-qna_system_message = """
-You are an assistant to a financial technology firm who answers user queries on 10-K reports from various indusrty players which contain detailed information about financial performance, risk factors, market trends, and strategic initiatives.
-User input will have the context required by you to answer user questions.
-This context will begin with the token: ###Context.
-The context contains references to specific portions of a document relevant to the user query.
-
-User questions will begin with the token: ###Question.
-
-Please answer only using the context provided in the input. Do not mention anything about the context in your final answer.
-
-If the answer is not found in the context, respond "I don't know".
-"""
-
-# Define the user message template
-qna_user_message_template = """
-###Context
-Here are some documents that are relevant to the question mentioned below.
-{context}
-
-###Question
-{question}
-"""
-
-
-
-# Define the predict function that runs when 'Submit' is clicked or when a API request is made
-def predict(user_input,company):
-
-    filter = "dataset/"+company+"-10-k-2023.pdf"
-    relevant_document_chunks = retriever.invoke(user_input, filter=filter)
-    context_list = [f"Page {doc.metadata['page']}: {doc.page_content}" for doc in docs]
-
-
-    # Create context_for_query
-    context_for_query = ".".join(context_list)
-
-
-
-    # Create messages
-    prompt = [
-        {'role':'system','content': qna_system_message},
-        {'role':'user', 'content': qna_user_message_template.format(context=context_for_query,
-                                                                    question=user_input
-                                                                    )
-        }
-    ]
-
-
-    # Get response from the LLM
-    try:
-      response = client.chat.completions.create(
-        model="mlabonne/NeuralHermes-2.5-Mistral-7B",
-        messages=prompt,
-        temperature=0
-      )
-
-      prediction = response.choices[0].message.content
-    except Exception as e:
-      prediction = f'Sorry, I encountered the following error: \n {e}'
-
-    print(prediction)
-
-
-    # While the prediction is made, log both the inputs and outputs to a local log file
-    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
-    # access
-
-    with scheduler.lock:
-        with log_file.open("a") as f:
-            f.write(json.dumps(
-                {
-                    'user_input': user_input,
-                    'retrieved_context': context_for_query,
-                    'model_response': prediction
-                }
-            ))
-            f.write("\n")
-
-    return prediction
-
-# Set-up the Gradio UI
-# Add text box and radio button to the interface
-# The radio button is used to select the company 10k report in which the context needs to be retrieved.
-
-textbox = gr.Textbox(placeholder = "Enter your query here")
-company = gr.Radio(choices=["IBM", "META", "aws", "google", "msft"], label="Company")
-
-# Create the interface
-# For the inputs parameter of Interface provide [textbox,company]
-demo = gr.Interface(
-    inputs = [textbox,company],
-    fn = predict,
-    outputs = "text",
-    description="This web API presents an interface to ask questions on contents of of IBM, META, AWS, GOOGLE and MSFT 10-K reports for the year 2023",
-    article= "Note that questions that are not relevant to the aforermentioned companies' 10-K reports will not be answered",
-    title = "Q&A for IBM, META, AWS, GOOG & MSFT 10-K Statements",
-    examples = [["Has the company made any significant acquisitions in the AI space, and how are these acquisitions being integrated into the company's strategy?", ""],
-                ["How much capital has been allocated towards AI research and development?", ""],
-                ["What initiatives has the company implemented to address ethical concerns sorrounding AI, such as faireness, accountability, and privacy?", ""],
-                ["How does the company plan to differentiate itself in the AI space relave to  competitors?", ""]],
-    concurrency_limit=16
-)
-
-demo.queue()
-demo.launch(share=True, debug =False)