Create app.py

app.py

@@ -0,0 +1,282 @@
+import gradio as gr
+import shutil
+import os
+
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import HuggingFacePipeline
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_openai import ChatOpenAI
+import torch
+import fitz
+from google.colab import userdata
+from dotenv import load_dotenv, set_key
+
+load_dotenv(dotenv_path=".env")
+
+list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2","gpt-4o-mini"]
+list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+
+# Load and split PDF document
+def load_doc():
+    # Processing for one document only
+    # loader = PyPDFLoader(file_path)
+    # pages = loader.load()
+    path="pdfs"
+    loaders = []
+    for file in os.listdir(path):
+      print(file)
+      print(type(file))
+      loader = PyPDFLoader(f"/content/pdfs/{file}")
+      loaders.append(loader)
+
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = 200,
+        chunk_overlap = 64
+    )
+
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+# Create vector database
+def create_db(splits):
+    embeddings = HuggingFaceEmbeddings()
+    vectordb = FAISS.from_documents(splits, embeddings)
+    return vectordb
+
+
+# Initialize langchain LLM chain
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+    if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct":
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model,
+            huggingfacehub_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN"),
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
+        )
+    elif llm_model== "gpt-4o-mini":
+        os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+        llm = ChatOpenAI(
+            model_name="gpt-4o-mini",
+            temperature = temperature,
+            max_tokens = max_tokens,
+        )
+    else:
+        llm = HuggingFaceEndpoint(
+            huggingfacehub_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN"),
+            repo_id=llm_model,
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
+        )
+
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key='answer',
+        return_messages=True
+    )
+
+    retriever=vector_db.as_retriever()
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff",
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
+    return qa_chain
+
+# Initialize database
+def initialize_database(list_file_obj, progress=gr.Progress()):
+    if not os.path.exists("pdfs"):
+      os.mkdir("pdfs")
+    for file_obj in list_file_obj:
+      shutil.copy(file_obj.name,"pdfs")
+    # Load document and create splits
+    doc_splits = load_doc()
+    # Create or load vector database
+    vector_db = create_db(doc_splits)
+    return vector_db, "Database created!"
+
+# Initialize LLM
+def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+    # print("llm_option",llm_option)
+    llm_name = list_llm[llm_option]
+    print("llm_name: ",llm_name)
+    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
+    return qa_chain, "QA chain initialized. Chatbot is ready!"
+
+
+def format_chat_history(message, chat_history):
+    formatted_chat_history = []
+    for user_message, bot_message in chat_history:
+        formatted_chat_history.append(f"User: {user_message}")
+        formatted_chat_history.append(f"Assistant: {bot_message}")
+    return formatted_chat_history
+
+
+def conversation(qa_chain, message, history):
+    formatted_chat_history = format_chat_history(message, history)
+    # Generate response using QA chain
+    response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
+    response_answer = response["answer"]
+    if response_answer.find("Helpful Answer:") != -1:
+        response_answer = response_answer.split("Helpful Answer:")[-1]
+    response_sources = response["source_documents"]
+    response_source1 = response_sources[0].page_content.strip()
+    response_source2 = response_sources[1].page_content.strip()
+    response_source3 = response_sources[2].page_content.strip()
+    # Langchain sources are zero-based
+    response_source1_page = response_sources[0].metadata["page"] + 1
+    response_source2_page = response_sources[1].metadata["page"] + 1
+    response_source3_page = response_sources[2].metadata["page"] + 1
+    # Append user message and response to chat history
+    new_history = history + [(message, response_answer)]
+    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
+
+def setup_gradio_interface():
+    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue = "sky")) as demo:
+        vector_db = gr.State()
+        qa_chain = gr.State()
+        gr.HTML("<center><h1>RAG PDF Chatbot</h1><center>")
+        gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents.\
+        <b>Please do not upload confidential documents.</b>
+        """)
+
+        def set_env_vars(openai_key, huggingface_token):
+              """將 API 金鑰設為環境變數並儲存至 .env"""
+              if openai_key:
+                  os.environ["OPENAI_API_KEY"] = openai_key
+                  set_key(".env", "OPENAI_API_KEY", openai_key)
+              if huggingface_token:
+                  os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingface_token
+                  set_key(".env", "HUGGINGFACEHUB_API_TOKEN", huggingface_token)
+              return "Environment variables set successfully!"
+
+        with gr.Tab("帳號輸入"):
+              with gr.Row():
+                  with gr.Column():
+                      gr.Markdown("<b>Step 1 - Input OpenAI API Key</b>")
+                      with gr.Row():
+                           openai_key_input = gr.Textbox(
+                              label="OpenAI API Key",
+                              placeholder="Enter your OpenAI API Key",
+                              value=os.getenv("OPENAI_API_KEY", ""),
+                              type="password",
+                          )
+                  with gr.Column():
+                      gr.Markdown("<b>Step 2 - Input HuggingFaceHub API Token</b>")
+                      with gr.Row():
+                            huggingface_token_input = gr.Textbox(
+                                label="HuggingFaceHub API Token",
+                                placeholder="Enter your HuggingFaceHub API Key",
+                                value=os.getenv("HUGGINGFACEHUB_API_TOKEN", ""),
+                                type="password",
+                           )
+                  submit_button = gr.Button("Submit")
+              status_output = gr.Label()
+
+        with gr.Tab("對話機器人"):
+              with gr.Row():
+                  with gr.Column():
+                      gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
+                      with gr.Row():
+                          document = gr.Files(height=300, file_count="multiple", label="Upload PDF documents")
+                      with gr.Row():
+                          db_btn = gr.Button("Create vector database")
+                      with gr.Row():
+                              db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status",
+                      gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>")
+                      with gr.Row():
+                          llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value = list_llm_simple[0], type="index") # info="Select LLM", show_label=False
+                      with gr.Row():
+                          with gr.Accordion("LLM input parameters", open=False):
+                              with gr.Row():
+                                  slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True)
+                              with gr.Row():
+                                  slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated",interactive=True)
+                              with gr.Row():
+                                      slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True)
+                      with gr.Row():
+                          qachain_btn = gr.Button("Initialize Question Answering Chatbot")
+                      with gr.Row():
+                              llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status",
+
+                  with gr.Column():
+                      gr.Markdown("<b>Step 2 - Chat with your Document</b>")
+                      chatbot = gr.Chatbot(height=505)
+                      with gr.Accordion("Relevent context from the source document", open=False):
+                          with gr.Row():
+                              doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
+                              source1_page = gr.Number(label="Page", scale=1)
+                          with gr.Row():
+                              doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
+                              source2_page = gr.Number(label="Page", scale=1)
+                          with gr.Row():
+                              doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
+                              source3_page = gr.Number(label="Page", scale=1)
+                      with gr.Row():
+                          msg = gr.Textbox(placeholder="Ask a question", container=True)
+                      with gr.Row():
+                          submit_btn = gr.Button("Submit")
+                          clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
+
+              # Preprocessing events
+              db_btn.click(initialize_database, \
+                  inputs=[document], \
+                  outputs=[vector_db, db_progress])
+              qachain_btn.click(initialize_LLM, \
+                  inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
+                  outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
+                  inputs=None, \
+                  outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
+                  queue=False)
+
+              # Chatbot events
+              msg.submit(conversation, \
+                  inputs=[qa_chain, msg, chatbot], \
+                  outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
+                  queue=False)
+              submit_btn.click(conversation, \
+                  inputs=[qa_chain, msg, chatbot], \
+                  outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
+                  queue=False)
+              clear_btn.click(lambda:[None,"",0,"",0,"",0], \
+                  inputs=None, \
+                  outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
+                  queue=False)
+
+        def set_env_vars(openai_key, huggingface_token):
+              """將 API 金鑰設為環境變數並儲存至 .env"""
+              if openai_key:
+                  os.environ["OPENAI_API_KEY"] = openai_key
+                  set_key(".env", "OPENAI_API_KEY", openai_key)
+              if huggingface_token:
+                  os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingface_token
+                  set_key(".env", "HUGGINGFACEHUB_API_TOKEN", huggingface_token)
+              return "Environment variables set successfully!"
+
+    # 綁定按鈕與設置環境變數的函數
+        submit_button.click(
+              set_env_vars,
+              inputs=[openai_key_input, huggingface_token_input],
+              outputs=[status_output]
+          )
+
+    return demo
+
+demo = setup_gradio_interface()
+demo.launch(debug=True)