app.py

import time
import json
import requests
import streamlit as st

st.set_page_config(page_title="ViBidLQA - Trợ lý AI hỗ trợ hỏi đáp luật Việt Nam", page_icon="./app/static/ai.jpg", layout="centered", initial_sidebar_state="collapsed")

routing_response_module = st.secrets["ViBidLQA_Routing_Module"]
retrieval_module = st.secrets["ViBidLQA_Retrieval_Module"]
ext_QA_module = st.secrets["ViBidLQA_EQA_Module"]
abs_QA_module = st.secrets["ViBidLQA_AQA_Module"]


url_api_question_classify_model = f"{routing_response_module}/query_classify"
url_api_unrelated_question_response_model = f"{routing_response_module}/response_unrelated_question"
url_api_introduce_system_modelff = f"{routing_response_module}/about_me"
url_api_retrieval_model = f"{retrieval_module}/search"
url_api_reranker_model = f"{reranker_module}/rerank"
url_api_generation_model = f"{abs_QA_module}/answer"

with open("./static/styles.css") as f:
    st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
    
if 'messages' not in st.session_state:
    st.session_state.messages = [{'role': 'assistant', 'content': "Xin chào. Tôi là trợ lý AI văn bản luật Đấu thầu Việt Nam được phát triển bởi Nguyễn Trường Phúc và các cộng sự. Rất vui khi được hỗ trợ bạn trong các vấn đề pháp lý tại Việt Nam!"}]

st.markdown(f"""
<div class=logo_area>
    <img src="./app/static/ai.jpg"/>
</div>
""", unsafe_allow_html=True)
st.markdown("<h2 style='text-align: center;'>ViBidLQA</h2>", unsafe_allow_html=True)

def classify_question(question):
    data = {
        "question": question
    }
    
    response = requests.post(url_api_question_classify_model, json=data)
    
    if response.status_code == 200:
        print(response)
        return response
    else:
        return f"Lỗi: {response.status_code} - {response.text}"

def introduce_system(question):
    data = {
        "question": question
    }

    response = requests.post(url_api_introduce_system_model, json=data, stream=True)

    if response.status_code == 200:
        return response
    else:
        return f"Lỗi: {response.status_code} - {response.text}"
    
def response_unrelated_question(question):
    data = {
        "question": question
    }

    response = requests.post(url_api_unrelated_question_response_model, json=data, stream=True)

    if response.status_code == 200:
        return response
    else:
        return f"Lỗi: {response.status_code} - {response.text}"

def retrieve_context(question, top_k=10):
    data = {
        "query": question,
        "top_k": top_k
    }

    response = requests.post(url_api_retrieval_model, json=data)

    if response.status_code == 200:
        results = response.json()["results"]
        return results
    else:
        return f"Lỗi tại Retrieval Module: {response.status_code} - {response.text}"

def rerank_context(url_rerank_module, question, relevant_docs, top_k=5):
    data = {
        "question": question,
        "relevant_docs": relevant_docs,
        "top_k": top_k
    }

    response = requests.post(url_rerank_module, json=data)

    if response.status_code == 200:
        results = response.json()["reranked_docs"]
        return results
    else:
        return f"Lỗi tại Rerank module: {response.status_code} - {response.text}"

def get_abstractive_answer(question):
    retrieved_context = retrieve_context(question=question)
    retrieved_context = [item['text'] for item in retrieved_context]

    reranked_context = rerank_context(url_rerank_module=url_api_reranker_model,
                                      question=question,
                                      relevant_docs=retrieved_context,
                                      top_k=5)[0]
    
    data = {
        "context": reranked_context,
        "question": question
    }

    response = requests.post(url_api_generation_model, json=data, stream=True)

    if response.status_code == 200:
        return response
    else:
        return f"Lỗi: {response.status_code} - {response.text}"

def generate_text_effect(answer):
    words = answer.split()
    for i in range(len(words)):
        time.sleep(0.03)
        yield " ".join(words[:i+1])

for message in st.session_state.messages:
    if message['role'] == 'assistant':
        avatar_class = "assistant-avatar"
        message_class = "assistant-message"
        avatar = './app/static/ai.jpg'
    else:
        avatar_class = ""
        message_class = "user-message"
        avatar = ''
    st.markdown(f"""
    <div class="{message_class}">
        <img src="{avatar}" class="{avatar_class}" />
        <div class="stMarkdown">{message['content']}</div>
    </div>
    """, unsafe_allow_html=True)

if prompt := st.chat_input(placeholder='Tôi có thể giúp được gì cho bạn?'):
    st.markdown(f"""
    <div class="user-message">
            <div class="stMarkdown">{prompt}</div>
    </div>
    """, unsafe_allow_html=True)
    st.session_state.messages.append({'role': 'user', 'content': prompt})
    
    message_placeholder = st.empty()
    
    full_response = ""
    classify_result = classify_question(question=prompt).json()

    print(f"The type of user query: {classify_result}")

    if classify_result == "BIDDING_RELATED":
        abs_answer = get_abstractive_answer(question=prompt)

        if isinstance(abs_answer, str):
            full_response = abs_answer
            message_placeholder.markdown(f"""
            <div class="assistant-message">
                <img src="./app/static/ai.jpg" class="assistant-avatar" />
                <div class="stMarkdown">{full_response}</div>
            </div>
            """, unsafe_allow_html=True)
        else:
            full_response = ""
            for line in abs_answer.iter_lines():
                if line:
                    line = line.decode('utf-8')
                    if line.startswith('data: '):
                        data_str = line[6:]
                        if data_str == '[DONE]':
                            break
                        
                        try:
                            data = json.loads(data_str)
                            token = data.get('token', '')
                            full_response += token
                            
                            message_placeholder.markdown(f"""
                            <div class="assistant-message">
                                <img src="./app/static/ai.jpg" class="assistant-avatar" />
                                <div class="stMarkdown">{full_response}●</div>
                            </div>
                            """, unsafe_allow_html=True)
                            
                        except json.JSONDecodeError:
                            pass

    elif classify_result == "ABOUT_CHATBOT":
        answer = introduce_system(question=prompt)

        if isinstance(answer, str):
            full_response = answer
            message_placeholder.markdown(f"""
            <div class="assistant-message">
                <img src="./app/static/ai.jpg" class="assistant-avatar" />
                <div class="stMarkdown">{full_response}</div>
            </div>
            """, unsafe_allow_html=True)
        else:
            full_response = ""
            for line in answer.iter_lines():
                if line:
                    line = line.decode('utf-8')
                    if line.startswith('data: '):
                        data_str = line[6:]
                        if data_str == '[DONE]':
                            break
                        
                        try:
                            data = json.loads(data_str)
                            token = data.get('token', '')
                            full_response += token
                            
                            message_placeholder.markdown(f"""
                            <div class="assistant-message">
                                <img src="./app/static/ai.jpg" class="assistant-avatar" />
                                <div class="stMarkdown">{full_response}●</div>
                            </div>
                            """, unsafe_allow_html=True)
                            
                        except json.JSONDecodeError:
                            pass
    
    else:
        answer = response_unrelated_question(question=prompt)

        if isinstance(answer, str):
            full_response = answer
            message_placeholder.markdown(f"""
            <div class="assistant-message">
                <img src="./app/static/ai.jpg" class="assistant-avatar" />
                <div class="stMarkdown">{full_response}</div>
            </div>
            """, unsafe_allow_html=True)
        else:
            full_response = ""
            for line in answer.iter_lines():
                if line:
                    line = line.decode('utf-8')
                    if line.startswith('data: '):
                        data_str = line[6:]
                        if data_str == '[DONE]':
                            break
                        
                        try:
                            data = json.loads(data_str)
                            token = data.get('token', '')
                            full_response += token
                            
                            message_placeholder.markdown(f"""
                            <div class="assistant-message">
                                <img src="./app/static/ai.jpg" class="assistant-avatar" />
                                <div class="stMarkdown">{full_response}●</div>
                            </div>
                            """, unsafe_allow_html=True)
                            
                        except json.JSONDecodeError:
                            pass

    message_placeholder.markdown(f"""
    <div class="assistant-message">
        <img src="./app/static/ai.jpg" class="assistant-avatar" />
            <div class="stMarkdown">
                {full_response}
            </div>
    </div>
    """, unsafe_allow_html=True)
    
    st.session_state.messages.append({'role': 'assistant', 'content': full_response})