Update app.py

app.py

@@ -14,51 +14,13 @@ from langchain_community.llms import HuggingFacePipeline
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferMemory
 from langchain_community.llms import HuggingFaceEndpoint
-from huggingface_hub import InferenceClient
 import torch
-
 api_token = os.getenv("HF_TOKEN")
 
-client = InferenceClient(
-    "mistralai/Mistral-7B-Instruct-v0.3"
-)
-
 list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.3"]
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
-def format_prompt(message, history):
-    prompt = "<s>"
-    for user_prompt, bot_response in history:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-    prompt += f"[INST] {message} [/INST]"
-    return prompt
-
-def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=42,
-    )
-
-    formatted_prompt = format_prompt(prompt, history)
-
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    output = ""
-
-    for response in stream:
-        output += response.token.text
-        yield output
-    return output
-
+# Load PDF document and create doc splits
 def load_doc(list_file_path, chunk_size, chunk_overlap):
     loaders = [PyPDFLoader(x) for x in list_file_path]
     pages = []
@@ -68,6 +30,7 @@ def load_doc(list_file_path, chunk_size, chunk_overlap):
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
+# Create vector database
 def create_db(splits, collection_name, db_type):
     embedding = HuggingFaceEmbeddings()
     
@@ -100,8 +63,10 @@ def create_db(splits, collection_name, db_type):
     
     return vectordb
 
+# Initialize langchain LLM chain
 def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, initial_prompt, progress=gr.Progress()):
     progress(0.1, desc="Initializing HF tokenizer...")
+    
     progress(0.5, desc="Initializing HF Hub...")
 
     llm = HuggingFaceEndpoint(
@@ -177,30 +142,12 @@ def conversation(qa_chain, message, 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 conversation_no_doc(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=42,
-    )
-
-    formatted_prompt = format_prompt(prompt, history)
-
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    output = ""
-
-    for response in stream:
-        output += response.token.text
-        yield output
-    return output
+def conversation_no_doc(llm, message, history):
+    formatted_chat_history = format_chat_history(message, history)
+    response = llm({"question": message, "chat_history": formatted_chat_history})
+    response_answer = response["answer"]
+    new_history = history + [(message, response_answer)]
+    return llm, gr.update(value=""), new_history
 
 def upload_file(file_obj):
     list_file_path = []
@@ -208,33 +155,6 @@ def upload_file(file_obj):
         list_file_path.append(file.name)
     return list_file_path
 
-def initialize_database(list_file_obj, chunk_size, chunk_overlap, db_type, progress=gr.Progress()):
-    list_file_path = [x.name for x in list_file_obj if x is not None]
-    progress(0.1, desc="Creating collection name...")
-    collection_name = create_collection_name(list_file_path[0])
-    progress(0.25, desc="Loading document...")
-    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
-    progress(0.5, desc="Generating vector database...")
-    vector_db = create_db(doc_splits, collection_name, db_type)
-    progress(0.9, desc="Done!")
-    return vector_db, collection_name, "Complete!"
-
-def create_collection_name(filepath):
-    collection_name = Path(filepath).stem
-    collection_name = collection_name.replace(" ", "-")
-    collection_name = unidecode(collection_name)
-    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
-    collection_name = collection_name[:50]
-    if len(collection_name) < 3:
-        collection_name = collection_name + 'xyz'
-    if not collection_name[0].isalnum():
-        collection_name = 'A' + collection_name[1:]
-    if not collection_name[-1].isalnum():
-        collection_name = collection_name[:-1] + 'Z'
-    print('Filepath: ', filepath)
-    print('Collection name: ', collection_name)
-    return collection_name
-
 def demo():
     with gr.Blocks(theme="base") as demo:
         vector_db = gr.State()
@@ -309,58 +229,27 @@ def demo():
                 clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
 
         with gr.Tab("Step 6 - Chatbot without document"):
+            with gr.Row():
+                llm_no_doc_btn = gr.Radio(list_llm_simple, 
+                    label="LLM models", value=list_llm_simple[0], type="index", info="Choose your LLM model for chatbot without document")
+            with gr.Accordion("Advanced options - LLM model", open=False):
+                with gr.Row():
+                    slider_temperature_no_doc = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
+                with gr.Row():
+                    slider_maxtokens_no_doc = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
+                with gr.Row():
+                    slider_topk_no_doc = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
+            with gr.Row():
+                llm_no_doc_progress = gr.Textbox(value="None", label="LLM initialization for chatbot without document")
+            with gr.Row():
+                llm_no_doc_init_btn = gr.Button("Initialize LLM for Chatbot without document")
             chatbot_no_doc = gr.Chatbot(height=300)
-            additional_inputs=[
-                gr.Slider(
-                    label="Temperature",
-                    value=0.9,
-                    minimum=0.0,
-                    maximum=1.0,
-                    step=0.05,
-                    interactive=True,
-                    info="Higher values produce more diverse outputs",
-                ),
-                gr.Slider(
-                    label="Max new tokens",
-                    value=256,
-                    minimum=0,
-                    maximum=1048,
-                    step=64,
-                    interactive=True,
-                    info="The maximum numbers of new tokens",
-                ),
-                gr.Slider(
-                    label="Top-p (nucleus sampling)",
-                    value=0.90,
-                    minimum=0.0,
-                    maximum=1,
-                    step=0.05,
-                    interactive=True,
-                    info="Higher values sample more low-probability tokens",
-                ),
-                gr.Slider(
-                    label="Repetition penalty",
-                    value=1.2,
-                    minimum=1.0,
-                    maximum=2.0,
-                    step=0.05,
-                    interactive=True,
-                    info="Penalize repeated tokens",
-                )
-            ]
             with gr.Row():
                 msg_no_doc = gr.Textbox(placeholder="Type message to chat with lucIAna", container=True)
             with gr.Row():
                 submit_btn_no_doc = gr.Button("Submit message")
                 clear_btn_no_doc = gr.ClearButton([msg_no_doc, chatbot_no_doc], value="Clear conversation")
 
-            chat_interface = gr.ChatInterface(
-                fn=generate,
-                chatbot=chatbot_no_doc,
-                additional_inputs=additional_inputs,
-                title="Mistral 7B v0.3"
-            )
-
         # Preprocessing events
         db_btn.click(initialize_database, 
             inputs=[document, slider_chunk_size, slider_chunk_overlap, db_type_radio], 
@@ -368,7 +257,7 @@ def demo():
         set_prompt_btn.click(lambda prompt: gr.update(value=prompt), 
                              inputs=prompt_input, 
                              outputs=initial_prompt)
-        qachain_btn.click(initialize_llmchain, 
+        qachain_btn.click(initialize_LLM, 
             inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db, initial_prompt], 
             outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], 
             inputs=None, 
@@ -390,9 +279,13 @@ def demo():
             queue=False)
 
         # Initialize LLM without document for conversation
+        llm_no_doc_init_btn.click(initialize_llm_no_doc, 
+            inputs=[llm_no_doc_btn, slider_temperature_no_doc, slider_maxtokens_no_doc, slider_topk_no_doc, initial_prompt], 
+            outputs=[llm_no_doc, llm_no_doc_progress])
+
         submit_btn_no_doc.click(conversation_no_doc, 
-            inputs=[msg_no_doc, chatbot_no_doc], 
-            outputs=[msg_no_doc, chatbot_no_doc], 
+            inputs=[llm_no_doc, msg_no_doc, chatbot_no_doc], 
+            outputs=[llm_no_doc, msg_no_doc, chatbot_no_doc], 
             queue=False)
         clear_btn_no_doc.click(lambda:[None,""], 
             inputs=None,