Update

.gitignore

@@ -0,0 +1,24 @@
+# Virtual environment
+venv/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+
+# Compiled C extension
+*.so
+
+# Distribution / packaging
+dist/
+build/
+*.egg-info/
+
+# Local development
+db.sqlite3
+
+# IDE files
+.vscode/
+.idea/
+.env

Dockerfile

@@ -0,0 +1,20 @@
+# Sử dụng Python 3.9
+FROM python:3.9-slim
+
+# Thiết lập thư mục làm việc
+WORKDIR /app
+
+# Sao chép file requirements.txt vào thư mục làm việc
+COPY requirements.txt .
+
+# Cài đặt các dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Sao chép toàn bộ mã nguồn vào thư mục làm việc
+COPY . .
+
+# Mở cổng 7860, nơi FastAPI sẽ chạy
+EXPOSE 7860
+
+# Khởi chạy ứng dụng bằng uvicorn khi container được khởi động
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -7,4 +7,9 @@ sdk: docker
 pinned: false
 ---
 
+python3 -m venv venv
+venv\Scripts\activate
+pip freeze > requirements.txt  
+pip install -r requirements.txt
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

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+import os
+from fastapi import FastAPI, Query
+from pydantic import BaseModel
+from typing import List
+from huggingface_hub import InferenceClient
+from deep_translator import GoogleTranslator
+from sse_starlette.sse import EventSourceResponse
+from dotenv import load_dotenv, find_dotenv
+
+_ = load_dotenv(find_dotenv()) # read local .env file
+hf_api_key = os.environ['HF_TOKEN']
+
+app = FastAPI()
+
+# Initialize the InferenceClient and the translators
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1", token=hf_api_key)
+
+# translator_to_en = GoogleTranslator(source='vietnamese', target='english')
+# translator_to_ar = GoogleTranslator(source='english', target='vietnamese')
+
+class PromptRequest(BaseModel):
+    message: str
+    history: List[List[str]]
+
+class GenerateResponse(BaseModel):
+    output: str
+
+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_responses(response_stream):
+    for response in response_stream:
+        yield response.token.text
+
+@app.post("/generate")
+async def generate(prompt_request: PromptRequest, 
+             temperature: float = Query(0.9, ge=0.0, le=1.0),
+             max_new_tokens: int = Query(256, ge=0, le=1048),
+             top_p: float = Query(0.90, ge=0.0, le=1.0),
+             repetition_penalty: float = Query(1.2, ge=1.0, le=2.0),
+             stream: bool = Query(False, description="Set to True to return response stream, False to return full text")):
+    formatted_prompt = format_prompt(prompt_request.message, prompt_request.history)
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+
+    if stream:
+        response_stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+        return EventSourceResponse(generate_responses(response_stream), media_type="text/event-stream") # media_type="application/x-ndjson"
+    else:
+        response = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, details=True, return_full_text=False)
+        return response.generated_text
+
+@app.post("/translate")
+def translate(text: str, source: str, target: str):
+    if source == target:
+        return {"translated_text": text}
+
+    translator = GoogleTranslator(source=source, target=target)
+    translated_text = translator.translate(text)
+    return {"translated_text": translated_text}