Update app.py

app.py

@@ -1,64 +1,129 @@
+import os
+import subprocess
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer
+from optimum.intel.openvino import OVModelForCausalLM
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import warnings
 
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+warnings.filterwarnings(
+    "ignore",
+    category=DeprecationWarning,
+    message="__array__ implementation doesn't accept a copy keyword"
+)
 
+# 設定模型 ID 與轉換後存檔路徑（8-bit 量化版）
+model_id = "agentica-org/DeepScaleR-1.5B-Preview"
+export_path = "exported_model_openvino_int8"
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+print("Loading model as OpenVINO int8 (8-bit) model...")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+if os.path.exists(export_path) and os.listdir(export_path):
+    print(f"Found quantized OpenVINO model at '{export_path}', loading it...")
+    model = OVModelForCausalLM.from_pretrained(export_path, device_map="auto", use_cache=False)
+else:
+    print("No quantized model found, exporting and quantizing to OpenVINO int8 now...")
+    # 透過 optimum-cli 導出並量化模型（此命令行參數根據你的任務可能需要調整）
+    command = [
+        "optimum-cli", "export", "openvino",
+        "--model", model_id,
+        "--task", "text-generation",
+        "--weight-format", "int8",
+        export_path
+    ]
+    subprocess.run(command, check=True)
+    print(f"Quantized model saved to '{export_path}'.")
+    model = OVModelForCausalLM.from_pretrained(export_path, device_map="auto", use_cache=False)
 
-    messages.append({"role": "user", "content": message})
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
 
-    response = ""
+# 載入向量模型（用於將文本轉換為向量）
+encoder = SentenceTransformer("all-MiniLM-L6-v2")
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# 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.")
+]
 
-        response += token
-        yield response
+# 將 FAQ 問題轉換為向量
+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（使用 L2 距離）
+d = faq_vectors.shape[1]  # 向量維度
+index = faiss.IndexFlatL2(d)
+index.add(faq_vectors)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+# 對話歷史記錄
+history = []
+
+# 查詢函數：先嘗試從 FAQ 中檢索答案，若無匹配則使用 OpenVINO 模型生成回答
+def respond(prompt):
+    global history
+    # 將輸入轉換為向量，並使用 FAISS 查詢最相近的 FAQ 問題
+    query_vector = np.array(encoder.encode([prompt])).astype("float32")
+    D, I = index.search(query_vector, 1)
+    
+    if D[0][0] < 1.0:
+        response = faq_answers[I[0][0]]
+    else:
+        # 若 FAQ 無匹配，則使用 OpenVINO 模型生成回答
+        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})
+        
+        # 將對話訊息組成一個 prompt（以換行分隔）
+        chat_prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
+        model_inputs = tokenizer(chat_prompt, 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.decode(generated_ids[0], skip_special_tokens=True).strip()
+    
+    history.append((prompt, response))
+    return response
+
+# 清除對話歷史記錄
+def clear_history():
+    global history
+    history = []
+    return "History cleared!"
 
+# 建立 Gradio 介面
+with gr.Blocks() as demo:
+    gr.Markdown("# DeepScaleR-1.5B-Preview (OpenVINO int8) Chatbot with FAISS FAQ")
+    
+    with gr.Tabs():
+        with gr.TabItem("Chat"):
+            chat_interface = gr.Interface(
+                fn=respond,
+                inputs=gr.Textbox(label="Prompt", placeholder="Enter your message..."),
+                outputs=gr.Textbox(label="Response", interactive=False),
+                api_name="hchat",
+                title="DeepScaleR-1.5B-Preview (OpenVINO int8) Chatbot",
+                description="This chatbot first searches an FAQ database using FAISS, then uses an OpenVINO 8-bit model to generate a response if no FAQ match is found."
+            )
+    
+    with gr.Row():
+        clear_button = gr.Button("🧹 Clear History")
+    
+    clear_button.click(fn=clear_history, inputs=[], outputs=[])
 
 if __name__ == "__main__":
-    demo.launch()
+    print("Launching Gradio app...")
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)