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import time
import streamlit as st
st.set_page_config(page_title="چت بات ارتش", page_icon="🪖", layout="wide")
st.markdown("""
<style>
.main {
background-color: #f4f6f7;
}
.stChatMessage {
background-color: #e8f0fe;
border-radius: 12px;
padding: 10px;
margin-bottom: 10px;
}
</style>
""", unsafe_allow_html=True)
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.base import Embeddings
from langchain.vectorstores import FAISS
from langchain.indexes import VectorstoreIndexCreator
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from typing import List
from together import Together
class TogetherEmbeddings(Embeddings):
def __init__(self, model_name: str, api_key: str):
self.model_name = model_name
self.client = Together(api_key=api_key)
def embed_documents(self, texts: List[str]) -> List[List[float]]:
response = self.client.embeddings.create(
model=self.model_name,
input=texts
)
return [item.embedding for item in response.data]
def embed_query(self, text: str) -> List[float]:
return self.embed_documents([text])[0]
@st.cache_resource
def get_pdf_index():
with st.spinner('لطفاً لحظهای صبر کنید...'):
pdf_reader = [PyPDFLoader('C:/Users/ici/Desktop/test1.pdf')]
embeddings = TogetherEmbeddings(
model_name="togethercomputer/m2-bert-80M-8k-retrieval",
api_key="0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979"
)
return VectorstoreIndexCreator(
embedding=embeddings,
text_splitter=RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=0)
).from_loaders(pdf_reader)
index = get_pdf_index()
llm = ChatOpenAI(
base_url="https://api.together.xyz/v1",
api_key='0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979',
model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
)
chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type='stuff',
retriever=index.vectorstore.as_retriever(),
input_key='question'
)
# --- UI زیباسازی ---
col1, col2 = st.columns([1, 10])
with col1:
st.image("army.png", width=70)
with col2:
st.title('🤖 چتبات هوشمند ارتش')
if 'messages' not in st.session_state:
st.session_state.messages = []
if 'pending_prompt' not in st.session_state:
st.session_state.pending_prompt = None
for message in st.session_state.messages:
with st.chat_message(message['role']):
st.markdown(f"🗨️ {message['content']}", unsafe_allow_html=True)
prompt = st.chat_input('چطور میتونم کمک کنم؟')
if prompt:
st.session_state.messages.append({'role': 'user', 'content': prompt})
st.session_state.pending_prompt = prompt
st.rerun()
if st.session_state.pending_prompt:
with st.chat_message('ai'):
thinking_placeholder = st.empty()
thinking_placeholder.markdown("🤖 در حال فکر کردن...")
response = chain.run(f'persian {st.session_state.pending_prompt}')
helpful_answer = response.split("Helpful Answer:")[-1]
if not helpful_answer.strip():
helpful_answer = "اطلاعات دقیقی در دسترس نیست، اما میتوانم به شما کمک کنم تا از منابع دیگر بررسی کنید."
thinking_placeholder.empty()
full_response = ""
placeholder = st.empty()
for chunk in helpful_answer.split():
full_response += chunk + " "
placeholder.markdown(full_response + "▌")
time.sleep(0.03)
placeholder.markdown(full_response)
st.session_state.messages.append({'role': 'ai', 'content': full_response})
st.session_state.pending_prompt = None
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