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import streamlit as st |
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import datetime |
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import pandas as pd |
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from typing import List |
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from langchain.embeddings import Embeddings |
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from langchain.vectorstores import FAISS |
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from langchain.prompts import ChatPromptTemplate |
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from langchain.chat_models import ChatOpenAI |
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from langchain.chains import ChatChain |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.schema import Document |
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st.set_page_config(page_title="رزم‌یار ارتش", page_icon="🪖", layout="wide") |
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st.markdown(""" |
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<style> |
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@import url('https://fonts.googleapis.com/css2?family=Vazirmatn:wght@400;700&display=swap'); |
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html, body, [class*="css"] { |
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font-family: 'Vazirmatn', Tahoma, sans-serif; |
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direction: rtl; |
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text-align: right; |
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} |
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.stApp { |
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background: linear-gradient(to left, #4b5e40, #2e3b2e); |
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color: #ffffff; |
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} |
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/* استایل‌های دیگر در اینجا قرار دارند */ |
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</style> |
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""", unsafe_allow_html=True) |
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if "authenticated" not in st.session_state: |
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st.session_state.authenticated = False |
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if not st.session_state.authenticated: |
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st.markdown("<h3 style='text-align: center; color: #b8860b;'>ورود به رزم‌یار ارتش</h3>", unsafe_allow_html=True) |
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username = st.text_input("نام کاربری:", placeholder="شناسه نظامی خود را وارد کنید") |
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password = st.text_input("رمز عبور:", type="password", placeholder="رمز عبور نظامی") |
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if st.button("ورود"): |
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if username == "admin" and password == "123": |
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st.session_state.authenticated = True |
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st.rerun() |
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else: |
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st.error("نام کاربری یا رمز عبور اشتباه است.") |
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st.stop() |
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with st.sidebar: |
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st.image("log.png", use_container_width=True) |
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menu_items = [ |
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("گزارش عملیاتی", "https://cdn-icons-png.flaticon.com/512/3596/3596165.png"), |
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("تاریخچه ماموریت‌ها", "https://cdn-icons-png.flaticon.com/512/709/709496.png"), |
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("تحلیل داده‌های نظامی", "https://cdn-icons-png.flaticon.com/512/1828/1828932.png"), |
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("مدیریت منابع", "https://cdn-icons-png.flaticon.com/512/681/681494.png"), |
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("دستیار فرماندهی", "https://cdn-icons-png.flaticon.com/512/3601/3601646.png"), |
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("تنظیمات امنیتی", "https://cdn-icons-png.flaticon.com/512/2099/2099058.png"), |
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("پشتیبانی فنی", "https://cdn-icons-png.flaticon.com/512/597/597177.png"), |
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] |
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for idx, (text, icon) in enumerate(menu_items): |
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st.markdown(f""" |
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<div class="menu-item"> |
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<img src="{icon}" /> |
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{text} |
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</div> |
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""", unsafe_allow_html=True) |
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st.markdown(""" |
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<div class="header-text"> |
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<h1>رزم‌یار ارتش</h1> |
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<div class="subtitle">دستیار هوشمند ارتش</div> |
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</div> |
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""", unsafe_allow_html=True) |
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st.markdown(f""" |
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<div class="chat-message"> |
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<span style="font-size: 24px;">🪖</span> |
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<span>به رزم‌یار ارتش خوش آمدید.</span> |
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</div> |
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""", unsafe_allow_html=True) |
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class TogetherEmbeddings(Embeddings): |
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def __init__(self, model_name: str, api_key: str): |
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self.model_name = model_name |
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self.client = Together(api_key=api_key) |
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def embed_documents(self, texts: List[str]) -> List[List[float]]: |
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batch_size = 100 |
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embeddings = [] |
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for i in range(0, len(texts), batch_size): |
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batch = texts[i:i + batch_size] |
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response = self.client.embeddings.create(model=self.model_name, input=batch) |
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embeddings.extend([item.embedding for item in response.data]) |
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return embeddings |
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def embed_query(self, text: str) -> List[float]: |
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return self.embed_documents([text])[0] |
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@st.cache_resource |
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def build_vectorstore_from_csv(csv_file_path: str): |
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df = pd.read_csv(csv_file_path) |
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texts = df.iloc[:, 0].astype(str).tolist() |
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texts = [text.strip() for text in texts if text.strip()] |
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text_splitter = RecursiveCharacterTextSplitter( |
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chunk_size=2048, |
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chunk_overlap=256, |
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length_function=len, |
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separators=["\n\n", "\n", " ", ""] |
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) |
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split_texts = [] |
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for text in texts: |
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split_texts.extend(text_splitter.split_text(text)) |
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documents = [Document(page_content=text) for text in split_texts] |
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embeddings = TogetherEmbeddings( |
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model_name="togethercomputer/m2-bert-80M-32k-retrieval", |
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api_key='0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979' |
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) |
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vectorstore = FAISS.from_documents(documents, embeddings) |
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return vectorstore, embeddings |
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def load_llm(): |
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return ChatOpenAI( |
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base_url="https://api.together.xyz/v1", |
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api_key='0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979', |
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model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free" |
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) |
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def process_user_query(query: str, vectorstore, embedding_model, llm): |
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query_embedding = embedding_model.embed_query(query) |
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docs = vectorstore.similarity_search_by_vector(query_embedding, k=3) |
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context = "\n".join([doc.page_content for doc in docs]) |
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final_prompt = f"""با توجه به اطلاعات زیر، فقط بر اساس آن‌ها به سؤال پاسخ بده. اگر اطلاعات کافی نیست، بگو اطلاعات کافی ندارم. |
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🔹 اطلاعات:\n{context}\n\n❓ سؤال: {query} |
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""" |
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response = llm.invoke(final_prompt) |
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raw_answer = response.content.strip() |
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clean_answer = raw_answer.strip() or "متأسفم، اطلاعات دقیقی در این مورد ندارم." |
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return clean_answer |
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def run_chat_ui(): |
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csv_file_path = 'output (1).csv' |
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try: |
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vectorstore, embedding_model = build_vectorstore_from_csv(csv_file_path) |
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except Exception as e: |
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st.error(f"خطا در پردازش فایل: {str(e)}") |
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return |
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llm = load_llm() |
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with st.form(key="chat_form"): |
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user_input = st.text_area("دستور یا پرس‌وجو:", height=120, placeholder="ماموریت یا سوال خود را وارد کنید...") |
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submit_button = st.form_submit_button("ارسال دستور") |
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if submit_button and user_input: |
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response = process_user_query(user_input, vectorstore, embedding_model, llm) |
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st.markdown(f""" |
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<div class="chat-message"> |
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<span style="font-size: 24px;">🎖️</span> |
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<span>{response}</span> |
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</div> |
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""", unsafe_allow_html=True) |
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run_chat_ui() |
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