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()