Update app.py

app.py

@@ -1,8 +1,8 @@
 import gradio as gr
-
 import os
 api_token = os.getenv("HF_TOKEN")
 
+
 from langchain_community.vectorstores import FAISS
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
@@ -18,7 +18,11 @@ import torch
 list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]  
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
+# Load and split PDF document
 def load_doc(list_file_path):
+    # Processing for one document only
+    # loader = PyPDFLoader(file_path)
+    # pages = loader.load()
     loaders = [PyPDFLoader(x) for x in list_file_path]
     pages = []
     for loader in loaders:
@@ -30,11 +34,14 @@ def load_doc(list_file_path):
     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(
@@ -70,27 +77,36 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
     )
     return qa_chain
 
+# Initialize database
 def initialize_database(list_file_obj, progress=gr.Progress()):
+    # Create a list of documents (when valid)
     list_file_path = [x.name for x in list_file_obj if x is not None]
+    # Load document and create splits
     doc_splits = load_doc(list_file_path)
+    # 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:
@@ -99,11 +115,14 @@ def conversation(qa_chain, message, history):
     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 upload_file(file_obj):
     list_file_path = []
@@ -112,90 +131,86 @@ def upload_file(file_obj):
         list_file_path.append(file_path)
     return list_file_path
 
+
 def demo():
-    custom_css = """
-    #column-container {
-        display: flex;
-        flex-direction: row;
-        flex-wrap: nowrap;
-    }
-    #column-left {
-        min-width: 20px;
-        max-width: 50px;
-        margin-right: 10px;
-    }
-    #column-right {
-        min-width: 200px;
-        flex-grow: 1;
-    }
-    @media (max-width: 250px) {
-        #column-left { min-width: 20px; }
-        #column-right { min-width: 200px; }
-    }
-    """
-
-    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky"), css=custom_css) as demo:
+    # with gr.Blocks(theme=gr.themes.Default(primary_hue="sky")) as demo:
+    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.""")
-        
-        with gr.Row(elem_id="column-container"):
-            with gr.Column(elem_id="column-left"):
+        gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. \
+        <b>Please do not upload confidential documents.</b>
+        """)
+        with gr.Row():
+            with gr.Column(scale = 45):
                 gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
-                document = gr.Files(height=140, file_count="multiple", file_types=[".pdf"], interactive=True, label="Upload PDF documents")
-                db_btn = gr.Button("Create vector database")
-                db_progress = gr.Textbox(value="Not initialized", show_label=False)
-                gr.Markdown("<b>Select Large Language Model (LLM) and input parameters</b>")
-                llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
-                with gr.Accordion("LLM input parameters", open=False):
-                    slider_temperature = gr.Slider(0.01, 1.0, value=0.5, step=0.1, label="Temperature")
-                    slider_maxtokens = gr.Slider(128, 9192, value=4096, step=128, label="Max New Tokens")
-                    slider_topk = gr.Slider(1, 10, value=3, step=1, label="top-k")
-                qachain_btn = gr.Button("Initialize Question Answering Chatbot")
-                llm_progress = gr.Textbox(value="Not initialized", show_label=False)
-
-            with gr.Column(elem_id="column-right"):
+                with gr.Row():
+                    document = gr.Files(height=140, file_count="multiple", file_types=[".pdf"], interactive=True, 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(scale = 200):
                 gr.Markdown("<b>Step 2 - Chat with your Document</b>")
-                chatbot = gr.Chatbot(height=480)
-                with gr.Accordion("Relevant context from the source document", open=False):
+                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, scale=20)
+                        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, scale=20)
+                        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, scale=20)
+                        doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
                         source3_page = gr.Number(label="Page", scale=1)
-                msg = gr.Textbox(placeholder="Ask a question")
+                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")
-
-        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)
-        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)
-    
+            
+        # 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)
     demo.queue().launch(debug=True)
+    # demo.launch(debug=True)
 
 
 if __name__ == "__main__":