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from datetime import datetime
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_parse import LlamaParse
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
import os
from dotenv import load_dotenv
import gradio as gr
import markdowm as md
import base64
# Load environment variables
load_dotenv()
# Mapping for display names and actual model names
llm_display_names = {
"tiiuae/falcon-7b-instruct": "HundAI",
"mistralai/Mixtral-8x7B-Instruct-v0.1": "Mixtral-8x7B",
"meta-llama/Meta-Llama-3-8B-Instruct": "Meta-Llama-3",
"mistralai/Mistral-7B-Instruct-v0.2": "Mistral-7B",
}
# Reverse mapping to retrieve original names
llm_reverse_mapping = {v: k for k, v in llm_display_names.items()}
# Update UI to use display names
def set_llm_model(display_name):
global selected_llm_model_name
# Retrieve the original model name using the reverse mapping
selected_llm_model_name = llm_reverse_mapping.get(display_name, display_name)
print(f"Model selected: {selected_llm_model_name}")
# Respond function remains unchanged
def respond(message, history):
try:
# Initialize the LLM with the selected model
llm = HuggingFaceInferenceAPI(
model_name=selected_llm_model_name, # Use the backend model name
contextWindow=8192,
maxTokens=1024,
temperature=0.3,
topP=0.9,
frequencyPenalty=0.5,
presencePenalty=0.5,
token=os.getenv("TOKEN")
)
# Set up the query engine with the selected LLM
query_engine = vector_index.as_query_engine(llm=llm)
bot_message = query_engine.query(message)
print(f"\n{datetime.now()}:{selected_llm_model_name}:: {message} --> {str(bot_message)}\n")
return f"{selected_llm_model_name}:\n{str(bot_message)}"
except Exception as e:
if str(e) == "'NoneType' object has no attribute 'as_query_engine'":
return "Please upload a file."
return f"An error occurred: {e}"
# UI Setup
with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo:
gr.Markdown("")
with gr.Tabs():
with gr.TabItem("Introduction"):
gr.Markdown(md.description)
with gr.TabItem("Chatbot"):
with gr.Accordion("IMPORTANT: READ ME FIRST", open=False):
guid = gr.Markdown(md.guide)
with gr.Row():
with gr.Column(scale=1):
file_input = gr.File(file_count="single", type='filepath', label="Upload document")
embed_model_dropdown = gr.Dropdown(embed_models, label="Select Embedding", interactive=True)
with gr.Row():
btn = gr.Button("Submit", variant='primary')
clear = gr.ClearButton()
output = gr.Text(label='Vector Index')
# Use display names for LLM dropdown
llm_model_dropdown = gr.Dropdown(
list(llm_display_names.values()), # Display names
label="Select LLM",
interactive=True
)
with gr.Column(scale=3):
gr.ChatInterface(
fn=respond,
chatbot=gr.Chatbot(height=500),
show_progress='full',
textbox=gr.Textbox(placeholder="Ask me any questions on the uploaded document!", container=False)
)
# Set up Gradio interactions
llm_model_dropdown.change(fn=set_llm_model, inputs=llm_model_dropdown)
btn.click(fn=load_files, inputs=[file_input, embed_model_dropdown], outputs=output)
clear.click(lambda: [None] * 3, outputs=[file_input, embed_model_dropdown, output])
# Launch the demo with a public link option
if __name__ == "__main__":
demo.launch()
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