.s

app.py

@@ -1,85 +1,78 @@
 import time
 import streamlit as st
-st.set_page_config(page_title="هوش مصنوعی توانا", page_icon="army.png", layout="wide")
+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
+
+st.set_page_config(page_title="چت‌بات ارتش", page_icon="🪖", layout="wide")
+
 st.markdown("""
     <style>
     .main {
-        background-color: #f4f6f7;
+        background-color: #f9f9f9;
     }
 
     .stChatMessage {
-        background-color: #e8f0fe;
+        background-color: #e0f7fa;
         border-radius: 12px;
-        padding: 10px;
-        margin-bottom: 10px;
+        padding: 12px;
+        margin-bottom: 12px;
         direction: rtl;
         text-align: right;
         font-family: 'Tahoma', sans-serif;
     }
 
-    .stMarkdown, .stTextInput, .stTextArea {
+    .stMarkdown, .stTextInput, .stTextArea, .stButton {
         direction: rtl !important;
         text-align: right !important;
         font-family: 'Tahoma', sans-serif;
     }
 
-    .center-header {
+    .header-container {
         display: flex;
         flex-direction: column;
         align-items: center;
         justify-content: center;
-        margin-top: 50px;
-        margin-bottom: 30px;
+        margin-top: 30px;
+        margin-bottom: 40px;
     }
 
-    .center-header img {
-        width: 200px;  
-        height: auto;
+    .header-container img {
+        width: 140px;
         border-radius: 15px;
-        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
-        margin-bottom: 10px;
+        margin-bottom: 12px;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.2);
     }
 
-    .center-header h1 {
-        font-size: 32px;
-        font-weight: bold;
+    .header-container h1 {
+        font-size: 36px;
         color: #2c3e50;
         font-family: 'Tahoma', sans-serif;
-        text-align: center;
         margin: 0;
-        letter-spacing: 1px;
     }
     </style>
 """, unsafe_allow_html=True)
 
-# حالا در جای مناسب در برنامه:
 st.markdown("""
-    <div class="center-header">
-        <img src="army.png" alt="لوگو">
-        <h1> هوش مصنوعی توانا/h1>
+    <div class="header-container">
+        <img src="https://your-logo-url.com/logo.png" alt="لوگو">
+        <h1>هوش مصنوعی توانا</h1>
     </div>
 """, 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
-        )
+        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]:
@@ -87,8 +80,8 @@ class TogetherEmbeddings(Embeddings):
 
 @st.cache_resource
 def get_pdf_index():
-    with st.spinner('لطفاً لحظه‌ای صبر کنید...'):
-        pdf_reader = [PyPDFLoader('test1.pdf')]
+    with st.spinner('📄 در حال پردازش فایل PDF...'):
+        loader = [PyPDFLoader('test1.pdf')]
         embeddings = TogetherEmbeddings(
             model_name="togethercomputer/m2-bert-80M-8k-retrieval",
             api_key="0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979"
@@ -96,14 +89,16 @@ def get_pdf_index():
         return VectorstoreIndexCreator(
             embedding=embeddings,
             text_splitter=RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=0)
-        ).from_loaders(pdf_reader)
+        ).from_loaders(loader)
 
 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',
@@ -111,25 +106,17 @@ chain = RetrievalQA.from_chain_type(
     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)
+for msg in st.session_state.messages:
+    with st.chat_message(msg['role']):
+        st.markdown(f"🗨️ {msg['content']}", unsafe_allow_html=True)
 
-prompt = st.chat_input('چطور می‌تونم کمک کنم؟')
+prompt = st.chat_input("چطور می‌تونم کمک کنم؟")
 
 if prompt:
     st.session_state.messages.append({'role': 'user', 'content': prompt})
@@ -138,19 +125,19 @@ if prompt:
 
 if st.session_state.pending_prompt:
     with st.chat_message('ai'):
-        thinking_placeholder = st.empty()
-        thinking_placeholder.markdown("🤖 در حال فکر کردن...")
+        thinking = st.empty()
+        thinking.markdown("🤖 در حال فکر کردن...")
 
         response = chain.run(f'پاسخ را فقط به زبان فارسی بده. سوال: {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."
+        answer = response.split("Helpful Answer:")[-1].strip()
+        if not answer:
+            answer = "متأسفم، اطلاعات دقیقی در این مورد ندارم."
 
-        thinking_placeholder.empty()
+        thinking.empty()
         full_response = ""
         placeholder = st.empty()
-        for chunk in helpful_answer.split():
-            full_response += chunk + " "
+        for word in answer.split():
+            full_response += word + " "
             placeholder.markdown(full_response + "▌")
             time.sleep(0.03)