app.py

import gradio as gr
from transformers import AutoTokenizer
from optimum.intel import OVModelForCausalLM
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import warnings

warnings.filterwarnings("ignore", category=DeprecationWarning, message="__array__ implementation doesn't accept a copy keyword")

# 載入 OpenVINO 語言模型
model_id = "hsuwill000/DeepSeek-R1-Distill-Qwen-1.5B-openvino"
print("Loading model...")
model = OVModelForCausalLM.from_pretrained(model_id, device_map="auto")
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)

# 載入向量模型 (用來將文本轉換為向量)
encoder = SentenceTransformer("all-MiniLM-L6-v2")

# FAQ 知識庫 (問題 + 回答)
faq_data = [
    ("What is FAISS?", "FAISS is a library for efficient similarity search and clustering of dense vectors."),
    ("How does FAISS work?", "FAISS uses indexing structures to quickly retrieve the nearest neighbors of a query vector."),
    ("Can FAISS run on GPU?", "Yes, FAISS supports GPU acceleration for faster computation."),
    ("What is OpenVINO?", "OpenVINO is an inference engine optimized for Intel hardware."),
    ("How to fine-tune a model?", "Fine-tuning involves training a model on a specific dataset to adapt it to a particular task."),
    ("What is the best way to optimize inference speed?", "Using quantization and model distillation can significantly improve inference speed.")
]

# 轉換 FAQ 問題為向量
faq_questions = [q for q, _ in faq_data]
faq_answers = [a for _, a in faq_data]
faq_vectors = np.array(encoder.encode(faq_questions)).astype("float32")

# 建立 FAISS 索引
d = faq_vectors.shape[1]  # 向量維度
index = faiss.IndexFlatL2(d)
index.add(faq_vectors)

# 對話歷史記錄
history = []

# 查詢函數 (先檢索 FAQ，無匹配則交給模型)
def respond(prompt):
    global history
    
    # 將輸入轉換為向量，並用 FAISS 查詢
    query_vector = np.array(encoder.encode([prompt])).astype("float32")
    D, I = index.search(query_vector, 1)  # 找最相近的 FAQ
    
    if D[0][0] < 1.0:  # 設定相似度閾值 (數值越低代表越相似)  (5.0太大 啥問題都會丟給FAISS)
        response = faq_answers[I[0][0]]  # 直接回應 FAQ 答案
    else:
        # 若 FAQ 沒有匹配，則使用語言模型
        messages = [{"role": "system", "content": "Answer the question in English only."}]
        for user_text, assistant_text in history:
            messages.append({"role": "user", "content": user_text})
            messages.append({"role": "assistant", "content": assistant_text})
        messages.append({"role": "user", "content": prompt})
        
        text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
        generated_ids = model.generate(
            **model_inputs,
            max_new_tokens=512,
            temperature=0.7,
            top_p=0.9,
            do_sample=True
        )
        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
        
    history.append((prompt, response))
    return response

# 清除歷史記錄
def clear_history():
    global history
    history = []
    return "History cleared!"

# Gradio 介面
with gr.Blocks() as demo:
    gr.Markdown("# DeepSeek-R1-Distill-Qwen-1.5B-openvino with history,FAISS ")
    
    with gr.Tabs():
        with gr.TabItem("聊天"):
            chat_if = gr.Interface(
                fn=respond,
                inputs=gr.Textbox(label="Prompt", placeholder="請輸入訊息..."),
                outputs=gr.Textbox(label="Response", interactive=False),
                api_name="hchat",
                title="DeepSeek-R1 with FAISS FAQ",
                description="This chatbot first searches an FAQ database using FAISS, then responds using a language model if no match is found."
            )
    
    with gr.Row():
        clear_button = gr.Button("🧹 Clear History")
    
    clear_button.click(fn=clear_history, inputs=[], outputs=[])

if __name__ == "__main__":
    print("Launching Gradio app...")
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)