Create app.py

app.py

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+import openai
+import random
+import time
+import gradio as gr
+import os
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import DeepLake
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.document_loaders import PyPDFDirectoryLoader
+from langchain.memory import ConversationBufferMemory
+from langchain.llms import OpenAI
+
+def set_api_key(key):
+ os.environ["OPENAI_API_KEY"] = key
+ return f"Your API Key has been set to: {key}"
+
+def reset_api_key():
+ os.environ["OPENAI_API_KEY"] = ""
+ return "Your API Key has been reset"
+
+def get_api_key():
+ api_key = os.getenv("OPENAI_API_KEY")
+ return api_key
+
+def set_model(model):
+ os.environ["OPENAI_MODEL"] = model
+ return f"{model} selected"
+
+def get_model():
+ model = os.getenv("OPENAI_MODEL")
+ return model 
+
+def upload_file(files):
+ file_paths = [file.name for file in files]
+ return file_paths
+
+def create_vectorstore(files):
+ pdf_dir = files.name
+ pdf_loader = PyPDFDirectoryLoader(pdf_dir)
+ pdf_docs = pdf_loader.load_and_split()
+ text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+ texts = text_splitter.split_documents(pdf_docs)
+ embeddings = OpenAIEmbeddings()
+ db = DeepLake.from_documents(texts, dataset_path="./documentation_db", embedding=embeddings, overwrite=True)
+ return "Vectorstore Successfully Created"
+
+def respond(message, chat_history):
+
+ # Get embeddings
+ embeddings = OpenAIEmbeddings()
+ 
+ #Connect to existing vectorstore
+ db = DeepLake(dataset_path="./documentation_db", embedding_function=embeddings, read_only=True)
+ #Set retriever settings
+ retriever = db.as_retriever(search_kwargs={"distance_metric":'cos',
+ "fetch_k":10,
+ "maximal_marginal_relevance":True,
+ "k":10})
+
+ if len(chat_history) != 0: 
+ chat_history = [(chat_history[0][0], chat_history[0][1])]
+
+ model = get_model()
+ # Create ChatOpenAI and ConversationalRetrievalChain
+ model = ChatOpenAI(model=model)
+ qa = ConversationalRetrievalChain.from_llm(model, retriever)
+
+ bot_message = qa({"question": message, "chat_history": chat_history})
+ chat_history = [(message, bot_message["answer"])]
+ time.sleep(1)
+ return "", chat_history
+
+with gr.Blocks() as demo:
+ #create chat history 
+ chat_history = []
+ with gr.Row():
+ #create textbox for API input
+ api_input = gr.Textbox(label = "API Key",
+ placeholder = "Please provide your OpenAI API key here.")
+ #create textbox to validate API
+ api_key_status = gr.Textbox(label = "API Key Status",
+ placeholder = "Your API Key has not be set yet. Please enter your key.",
+ interactive = False)
+ #create button to submit API key
+ api_submit_button = gr.Button("Submit")
+ #set api_submit_button functionality
+ api_submit_button.click(set_api_key, inputs=api_input, outputs=api_key_status)
+ #create button to reset API key
+ api_reset_button = gr.Button("Clear API Key from session")
+ #set api_reset_button functionality
+ api_reset_button.click(reset_api_key, outputs=api_key_status)
+ 
+ with gr.Row():
+ with gr.Column():
+ #create dropdown to select model (gpt-3.5-turbo or gpt4)
+ model_selection = gr.Dropdown(
+ ["gpt-3.5-turbo", "gpt-4"], label="Model Selection", info="Please ensure you provide the API Key that corresponds to the Model you select!"
+ )
+ #create button to submit model selection
+ model_submit_button = gr.Button("Submit Model Selection")
+ model_status = gr.Textbox(label = "Selected Model", interactive = False, lines=4)
+ #set model_submit_button functionality
+ model_submit_button.click(set_model, inputs=model_selection, outputs=model_status)
+ 
+ file_output = gr.File(label = "Uploaded files - Please note these files are persistent and will not be automatically deleted")
+ upload_button = gr.UploadButton("Click to Upload a PDF File", file_types=["pdf"], file_count="multiple")
+ upload_button.upload(upload_file, upload_button, file_output)
+ create_vectorstore_button = gr.Button("Click to create the vectorstore for your uploaded documents")
+ db_output = gr.Textbox(label = "Vectorstore Status")
+ create_vectorstore_button.click(create_vectorstore, inputs=file_output, outputs = db_output)
+ 
+ chatbot = gr.Chatbot(label="ChatGPT Powered Grant Writing Assistant")
+ msg = gr.Textbox(label="User Prompt", placeholder="Your Query Here")
+ clear = gr.Button("Clear")
+ 
+ msg.submit(respond, inputs = [msg, chatbot], outputs = [msg, chatbot])
+ clear.click(lambda: None, None, chatbot, queue=False)
+ 
+ 
+demo.launch()