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| # from unsloth import FastLanguageModel | |
| # import torch | |
| import os | |
| from llama_index.core.storage.storage_context import StorageContext | |
| from llama_index.core.indices.loading import load_index_from_storage | |
| from openai import OpenAI | |
| import gradio as gr | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| # model,tokenizer = FastLanguageModel.from_pretrained('./unified_model') | |
| # client = FastLanguageModel.for_inference(model) | |
| dir = 'aila_indices' | |
| # Initialize OpenAI client | |
| client = OpenAI() | |
| storage_context = StorageContext.from_defaults(persist_dir=dir) | |
| index = load_index_from_storage(storage_context) | |
| query_engine = index.as_query_engine(similarity_top_k=8) | |
| # def generate_response(instruction,chat_history): | |
| # """Generates a response using your fine-tuned model.""" | |
| # # FastLanguageModel.for_inference(model) # Enable native 2x faster inference within the function | |
| # prompt = f"""### Instruction: | |
| # Answer the following question. | |
| # ### Question: | |
| # {instruction} | |
| # Provide a unique, concise, and non-repetitive answer. | |
| # ### Answer:""" | |
| # inputs = tokenizer(prompt, return_tensors="pt").to("cuda") | |
| # with torch.no_grad(): | |
| # outputs = model.generate(**inputs,early_stopping=True,min_length=50,length_penalty=2,do_sample=True,max_new_tokens=300, | |
| # top_p=0.95, | |
| # top_k=50, | |
| # temperature=0.7, | |
| # repetition_penalty=1.2, | |
| # num_return_sequences=1 | |
| # ) | |
| # response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # response = response.split("### Answer:")[-1] | |
| # return response | |
| def generate_response_rag_index(instruction,chat_history): | |
| response = query_engine.query(instruction) | |
| return str(response) | |
| def update_chat_history(chat_history, user_message, bot_message): | |
| """Update chat history to maintain relevance and avoid excessive growth.""" | |
| chat_history['user'].append(user_message) | |
| chat_history['bot'].append(bot_message) | |
| # Keep only the last N interactions | |
| if len(chat_history['user']) > 5: | |
| chat_history['user'] = chat_history['user'][-5:] | |
| chat_history['bot'] = chat_history['bot'][-5:] | |
| return chat_history | |
| def chatbot(input_text,chat_history): | |
| messages = { | |
| "user": [], | |
| "bot": [], | |
| } | |
| for user_msg, bot_msg in chat_history: | |
| messages["user"].append(user_msg) | |
| messages["bot"].append(bot_msg) | |
| # bot_response = generate_response(input_text,messages) | |
| bot_response = generate_response_rag_index(input_text,messages) | |
| chat_history.append(("User: " + input_text, bot_response)) | |
| messages = update_chat_history(messages, input_text, bot_response) | |
| return "", chat_history | |
| with gr.Blocks() as demo: | |
| gr.Markdown('## AILA INTERFACE DEMO') | |
| with gr.Row(): | |
| gr.Image(value="up_2017_logo_en.png", interactive=False, label="Upatras Logo",width=150,height=100) | |
| gr.Image(value="aila_new.png", interactive=False, label="AILA project Logo",width=150,height=100) | |
| gr.Image(value="banner-horizontal-default-en.png", interactive=False, label="AUTH Logo",width=150,height=100) | |
| with gr.Row(): | |
| user_input = gr.Textbox( | |
| placeholder = "Type your message here...", | |
| label = "Your Message", | |
| lines = 1 | |
| ) | |
| submit_button = gr.Button('Submit') | |
| chat_history = gr.Chatbot() | |
| submit_button.click( | |
| chatbot, | |
| inputs = [user_input,chat_history], | |
| outputs = [user_input, chat_history] | |
| ) | |
| demo.launch() | |