Create app.py

app.py

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+## Setup
+!pip -q install gradio
+
+# Install the necessary libraries
+!pip install -q openai==1.23.2 \
+                tiktoken==0.6.0 \
+                pypdf==4.0.1 \
+                langchain==0.1.1 \
+                langchain-community==0.0.13 \
+                chromadb==0.4.22 \
+                sentence-transformers==2.3.1
+
+# Import the necessary libraries
+import gradio as gr
+import os
+import uuid
+import json
+import tiktoken
+import pandas as pd
+from openai import OpenAI
+from huggingface_hub import HfApi
+from huggingface_hub import CommitScheduler
+from langchain_community.embeddings.sentence_transformer import (
+    SentenceTransformerEmbeddings
+)
+from langchain_community.vectorstores import Chroma
+from google.colab import userdata, drive
+from pathlib import Path
+from langchain.document_loaders import PyPDFDirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import json
+import tiktoken
+import pandas as pd
+
+
+
+# Define the embedding model and the vectorstore
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
+
+# If dataset directory exixts, remove it and all of the contents within
+
+if os.path.exists('dataset'):
+  !rm -rf dataset
+
+# If collection_db exists, remove it and all of the contents within
+
+if os.path.exists('collection_db'):
+    !rm -rf dataset
+
+#Upload Dataset-10k.zip and unzip it dataset folder using -d option
+!unzip Dataset-10k.zip -d dataset
+
+# Provide pdf_folder_location
+pdf_folder_location = "dataset"
+
+# Load the directory to pdf_loader
+pdf_loader = PyPDFDirectoryLoader(pdf_folder_location)
+
+# Create text_splitter using recursive splitter
+text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+    encoding_name='cl100k_base',
+    chunk_size=512,
+    chunk_overlap=16
+)
+
+# Create chunks
+report_chunks = pdf_loader.load_and_split(text_splitter)
+
+#Create a Colelction Name
+collection_name = 'collection'
+
+# Create the vector Database
+vectorstore = Chroma.from_documents(
+    report_chunks,
+    embedding_model,
+    collection_name=collection_name,
+    persist_directory='./collection_db'
+)
+
+# Persist the DB
+vectorstore.persist()
+
+vectorstore_persisted = Chroma(
+    collection_name=collection_name,
+    persist_directory='./collection_db',
+    embedding_function=embedding_model
+)
+
+retriever = vectorstore_persisted.as_retriever(
+    search_type='similarity',
+    search_kwargs={'k': 5}
+)
+
+#Mount the Google Drive
+drive.mount('/content/drive')
+
+#Copy the persisted database to your drive
+!cp -r collection_db /content/drive/MyDrive/
+
+# Get anyscale api key
+anyscale_api_key = userdata.get('dev-work')
+
+# Initialise the client
+client = OpenAI(
+    base_url="https://api.endpoints.anyscale.com/v1",
+    api_key=anyscale_api_key
+)
+#Provide the model name
+model_name = 'mlabonne/NeuralHermes-2.5-Mistral-7B'
+
+# Initialise the embedding model
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
+
+# Load the persisted DB
+persisted_vectordb_location = '/content/drive/MyDrive/collection_db'
+
+#Create a Colelction Name
+collection_name = 'collection'
+
+# Load the persisted DB
+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="kgauvin603/rag-10k-analysis",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2,
+    token=hf_token
+)
+
+# Define the Q&A system message
+qna_system_message = """You are an assistant to a financial services firm who answers user queries on annual reports.
+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".
+"""
+
+# Create a 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 an API request is made
+def predict(user_input, company):
+
+    filter = "dataset/" + company + "-10-k-2023.pdf"
+    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source": filter})
+
+    # Create context_for_query
+    context_list = [d.page_content for d in relevant_document_chunks]
+    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
+        )}
+    ]
+
+    try:
+        response = client.chat.completions.create(
+            model=model_name,
+            messages=prompt,
+            temperature=0
+        )
+
+        prediction = response.choices[0].message.content.strip()
+    except Exception as e:
+        prediction = f'Sorry, I encountered the following error: \n{e}'
+
+    # Log both the inputs and outputs to a local 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(label="User Input")
+#company = gr.List(label="Select Company", choices=["IBM", "Meta", "aws", "google","msft"])
+company = gr.Dropdown(label="Select Company", choices=["IBM", "Meta", "aws", "google","msft"])
+
+# Create the interface
+# For the inputs parameter of Interface provide [textbox, company]
+demo = gr.Interface(fn=predict, inputs=[textbox, company], outputs="text")
+
+demo.queue()
+demo.launch(share=True)