Update app.py

app.py

@@ -1,23 +1,156 @@
-import os
-import time
+
+## Setup
+# Import the necessary Libraries
 import json
+import tiktoken
+import os
+import pandas as pd
 import uuid
 import gradio as gr
+from openai import OpenAI
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+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
+client = OpenAI()
+
+# Define the embedding model and the vectorstore
+collection_name = 'project3_rag_db'
+embedding_model_name = 'thenlper/gte-large'
+embedding_model = SentenceTransformerEmbeddings(model_name=embedding_model_name)
+persisted_vectordb_location = '/content/drive/MyDrive/project3_rag_db'
+model_name = 'gpt-4o-mini'
+# Load the persisted vectorDB
+vectorstore_persisted = Chroma(
+    collection_name=collection_name,
+    persist_directory=persisted_vectordb_location,
+    embedding_function=embedding_model)
+
+# Prepare the logging functionality
+
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="anirudhabokil/project3_rag_10K_chatbot_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 Analyst for a Fin tech company. Your task is to provide relevant information about analysis of key information from 10-K reports.
+10-K reports are comprehensive annual reports filed by publicly traded companies in the United States with the Securities and Exchange Commission (SEC).
+User input will include the necessary context for you to answer their questions. This context will begin with the token: ###Context.
+The context contains references to specific portions of documents relevant to the user's query, along with source links.
+The source for a context will begin with the token ###Source:
+
+When crafting your response:
+1. Select only context relevant to answer the question.
+2. Include the source links in your response.
+3. User questions will begin with the token: ###Question.
+4. If the question is irrelevant to 10-K reports respond with - "I am an assistant to a Financial Analyst. I can only help you with questions related to 10-K reports"
+
+Please adhere to the following guidelines:
+- Your response should only be about the question asked and nothing else.
+- Answer only using the context provided.
+- Do not mention anything about the context in your final answer.
+- If the answer is not found in the context, it is very very important for you to respond with "I don't know."
+- Always quote the source when you use the context. Cite the relevant source at the end of your response under the section - Source:
+- 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.
+
+Here is an example of how to structure your response:
+
+Answer:
+[Answer]
+
+Source:
+[Use the ###Source provided in the context as it. Do not add https prefix]
+"""
+
+# Define the user message template
+qna_user_message_template = """
+###Context
+Here are some 10-K reports and their source links 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 = "/content/dataset/"+company+"-10-k-2023.pdf"
+    print(filter)
+    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source":filter})
+    print(relevant_document_chunks)
+    # Create context_for_query
+    context_list = [d.page_content + "\n ###Source: " + d.metadata['source'] + '\n\n ' for d in relevant_document_chunks]
+    context_for_query = ". ".join(context_list)
+    print(context_for_query)
+
+    # Create messages
+    prompt = [
+        {'role': 'system', 'content': qna_system_message},
+        {'role': 'user', 'content': qna_user_message_template.format(
+            context=context_for_query,
+            question=user_input
+            )
+        }]
+
+    print(prompt)
+    # Get response from the LLM
+
+    try:
+      response = client.chat.completions.create(model=model_name,messages=prompt,temperature=0)
+      print(response)
+      answer = response.choices[0].message.content.strip()
+    # Handle errors using try-except
+    except Exception as e:
+      answer = f'Sorry, I encountered the following error: \n {e}'
+
+    # 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': answer
+                }
+            ))
+            f.write("\n")
 
-user_question = gr.Textbox(label="Ask your question")
-model_output = gr.Label(label="Answer")
+    return answer
 
-def qa_bot(user_question):
-  return os.environ["OPENAI_BASE_URL"] + " Answer"
+# 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.
 
-demo = gr.Interface(
-    fn=qa_bot,
-    inputs=user_question,
-    outputs=model_output,
-    title="Ask your question",
-    flagging_mode="auto",
-    concurrency_limit=8)
+user_input = gr.Textbox(label="Ask your question")
+company =  gr.Dropdown(['aws','google','IBM','Meta','msft'], label="Company")
+answer = gr.Label(label="Answer")
+# Create the interface
+# For the inputs parameter of Interface provide [textbox,company]
+demo = gr.Interface(fn=predict,
+                    inputs=[user_input, company],
+                    outputs=answer,
+                    title="10-K Chatbot",
+                    description="This API answers questions based on 10-k reports",
+                    flagging_mode="auto",
+                    concurrency_limit=8)
 
 demo.queue()
 demo.launch(share=True, debug=True)
-# demo.close()