app.py

import gradio as gr
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain.agents import load_tools
import time
from time import sleep
from css import *
import replicate
from pre import *
from search import *
from post import *

os.environ["REPLICATE_API_TOKEN"] = "r8_IYJpjwjrxegcUfBeBbyUxErJXXsnHDM4AlSQQ"
os.environ["OPENAI_API_KEY"] = "sb-6a683cb3bd63a9b72040aa2dd08feff8b68f08a0e1d959f5"
os.environ['OPENAI_BASE_URL'] = "https://api.openai-sb.com/v1/"
os.environ["SERPAPI_API_KEY"] = "dcc98b22d5f7d413979a175ff7d75b721c5992a3ee1e2363020b2bbdf4f82404"
embedding_model = OpenAIEmbeddings()
tools_google = load_tools(["serpapi"])
llm=ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0.6)
flag="None"
selected_model = None
def process_option(option):
    global selected_model, llm, flag  # 使用全局变量
    llm= ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0.6)
    if option in ["gpt-3.5-turbo-0125", "gpt-4o", "gpt-4o-mini","None"]:
        selected_model = option
        llm = ChatOpenAI(model=selected_model, temperature=0.6)
    elif option=="llama-3-70b":
        flag="llama-3-70b"
    elif option == "llama-3-8b":
        flag = "llama-3-8b"
    elif option=="mistral":
        flag = "mistral"
    return selected_model

options = ["gpt-3.5-turbo-0125", "gpt-4o", "gpt-4o-mini", "llama-3-70b","llama-3-8b","mistral","None"]

selected_search = None
def search_option(option):
    global selected_search
    if option in ["bing_news","baidu_news","google_news"]:
        selected_search=option

search_options=["bing_news","baidu_news","google_news"]
def update_ans_box_label(selected_model):
    return gr.update(label=selected_model)

origin_prompt = ChatPromptTemplate.from_template(template)

def detect_fake_news(news,context,progress=gr.Progress()):
    progress(0, desc="初始化")
    sleep(1)  # Simulate some processing time
    chain = origin_prompt | llm | StrOutputParser()
    ori_result=chain.invoke(news)
    progress(0.25, desc="分割子句")
    sleep(1)
    cap_result=capture_sub_queries(news)
    progress(0.50, desc="提取相关信息")
    sleep(1)
    information=context+baidu(cap_result)
    result = detect_chain.invoke({"news": news, "text": information})
    result = extract_clauses_with_counts(result)
    if (result.count.get("不确定的子句个数")):
        if selected_search == "bing_news":
            search_text=search_bing(news)
        elif selected_search=="baidu_news":
            search_text=search_baidu(news)
        elif selected_search=="google_news":
            search_text=search_google(news)
        information = search_text + information
    progress(0.75, desc="Running main chain...")
    sleep(1)

    if flag=="None":
        rag_chain = rag_prompt | llm | StrOutputParser()
        rag_result = rag_chain.invoke({"news": news,"information":information})


    else:
        input = {
                    "top_p": 0.9,
                    "prompt": rag_prompt.format(
                        news=news,
                        information=information
                    ),
                    "min_tokens": 0,
                    "temperature": 0.6,
                    "prompt_template": "system\n\nYou are a helpful assistantuser\n\n{prompt}assistant\n\n",
                    "presence_penalty": 1.15
                }
        if flag == "llama-3-70b":
            output = replicate.run("meta/meta-llama-3-70b-instruct", input=input)
        elif flag == "llama-3-8b":
            output = replicate.run("meta/meta-llama-3-8b-instruct", input=input)
        elif flag == "mistral":
            output = replicate.run("mistralai/mixtral-8x7b-instruct-v0.1", input=input)

        rag_result = "".join(output)

    progress(1, desc="完成")
    rag_result=json.loads(rag_result)
    rag_result="这是"+rag_result['label']+","+rag_result['explanation']
    return cap_result,information, ori_result, rag_result
def toggle_visibility(is_visible):
    # 切换文本框的可见性
    new_visibility = not is_visible
    return gr.update(visible=new_visibility),gr.update(visible=new_visibility), new_visibility

current_css = css1

def toggle_style():
    global current_css
    # 切换CSS样式
    if current_css == css1:
        current_css = css2
    else:
        current_css = css1
    # 返回更新后的HTML内容
    return f'<style id="dynamic-css">{current_css}</style>'

with gr.Blocks(css=current_css) as iface:
    gr.Markdown("<h1 style='font-size: 36px; text-align: center; color: #333333; margin-bottom: 20px;'>虚假信息检测</h1>")
    is_visible = gr.State(False)
    html_output = gr.HTML(f'<style id="dynamic-css">{current_css}</style>')
    with gr.Row():
        with gr.Column(scale=2):
            chatbot=gr.Chatbot(elem_classes="gradio-output")
            context_box = gr.Textbox(label="上下文", elem_classes="gradio-input",placeholder="请输入与该新闻有关的上下文", lines=2)
            input_box = gr.Textbox(label="新闻", elem_classes="gradio-input",placeholder="请输入新闻", lines=3)
        with gr.Column(scale=1):
            option_select = gr.Dropdown(choices=options, label="选择模型",elem_classes="gradio-output")
            output = gr.Textbox(label="选择模型",visible=False)
            search_select=gr.Dropdown(choices=search_options,label="选择搜索api",elem_classes="gradio-output")
            ans_box = gr.Textbox(label="gpt-3.5-turbo-0125",lines=5,elem_classes="gradio-output")
            rag_box = gr.Textbox(label="有RAG增强",visible=False)
            # trans_box=gr.Textbox(label="transform",visible=False)
            capture_box=gr.Textbox(label="子句划分结果",visible=False,lines=3)
            information_box=gr.Textbox(label="提取到的信息",visible=False,lines=3)
            css_btn = gr.Button("白天（夜晚）模式切换", elem_classes="gradio-button")
            toggle_btn = gr.Button("显示（隐藏）文本框",elem_classes="gradio-button")
            clear = gr.Button("清空页面",elem_classes="gradio-button")
            submit_btn = gr.Button("提交",elem_classes="gradio-button")
        option_select.change(process_option, option_select, output).then(
            update_ans_box_label, inputs=output, outputs=ans_box
        )
        search_select.change(search_option,search_select)
        toggle_btn.click(toggle_visibility, [is_visible], [capture_box,information_box, is_visible])

        css_btn.click(toggle_style, inputs=None, outputs=html_output)
    def user(user_input, user_context, history):
        if history is None:
            history = []
        return "", "", history + [[user_input, None]]

    def bot(history,rag_box):
        if history is None or len(history) == 0:
            return
        bot_rag = str(rag_box)
        history[-1][1] = ""
        for character in bot_rag:
            history[-1][1] += character
            time.sleep(0.01)
            yield history

    submit_btn.click(detect_fake_news,[input_box,context_box],[capture_box,information_box,ans_box,rag_box]).then(
        user,[input_box,context_box,chatbot],[input_box,context_box,chatbot]
    ).then(
        bot,[chatbot,rag_box],chatbot
    )
    clear.click(lambda: (None, None, []), inputs=None, outputs=[chatbot, ans_box, rag_box], queue=False)

if __name__ == "__main__":
    iface.queue()
    app, local_url, share_url = iface.launch()
    # print(app, local_url, share_url)