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PdfChatbot.py

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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