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import os
import gradio as gr
import requests
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.document_loaders import UnstructuredHTMLLoader
from langchain.text_splitter import CharacterTextSplitter
# API Key 從環境變數讀取
GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
GROQ_MODEL = "llama3-70b-8192"
#GROQ_MODEL = "gemma2-9b-it"
embedding_model = HuggingFaceEmbeddings(model_name="BAAI/bge-base-zh")
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=100)
def build_vector_db_from_local_html(folder_path="data"):
all_docs = []
for filename in os.listdir(folder_path):
if filename.endswith(".html"):
loader = UnstructuredHTMLLoader(os.path.join(folder_path, filename))
docs = loader.load()
chunks = text_splitter.split_documents(docs)
all_docs.extend(chunks)
db = FAISS.from_documents(all_docs, embedding_model)
return db
db = build_vector_db_from_local_html()
def rag_chat(user_input):
docs = db.similarity_search(user_input, k=3)
context = "\n\n".join([doc.page_content for doc in docs])
messages = [
{"role": "system", "content": "你是一個親切的諮詢師，多和使用者聊天，且幫助使用者了解數位性暴力並提供協助。請使用繁體中文回答。"},
{"role": "user", "content": f"以下是相關資料：\n{context}\n\n請回答這個問題：{user_input}"}
]
headers = {"Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json"}
payload = {"model": GROQ_MODEL, "messages": messages}
try:
res = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=payload)
res.raise_for_status()
return res.json()["choices"][0]["message"]["content"]
except Exception as e:
return f"錯誤：{str(e)}"
iface = gr.Interface(fn=rag_chat,
inputs=gr.Textbox(label="輸入你的問題", placeholder="請輸入問題...", lines=3),
outputs="text",
title="AI諮詢機器人",
description="詢問我關於數位性暴力的事情，或者你遇到甚麼困境？")
iface.launch()
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