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import streamlit as st |
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import time |
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import numpy as np |
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from langchain.document_loaders import PyPDFLoader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain_together import TogetherEmbeddings |
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from langchain.chat_models import ChatOpenAI |
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from sklearn.metrics.pairwise import cosine_similarity |
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@st.cache_resource |
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def load_chunks_and_embeddings(): |
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with st.spinner('📄 در حال پردازش PDF و ساخت امبدینگها...'): |
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loader = PyPDFLoader('test1.pdf') |
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pages = loader.load() |
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splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=0) |
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chunks = splitter.split_documents(pages) |
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embeddings_model = TogetherEmbeddings( |
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api_key="0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979" |
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) |
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chunk_texts = [chunk.page_content for chunk in chunks] |
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chunk_embeddings = embeddings_model.embed_documents(chunk_texts) |
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st.success(f"✅ تعداد {len(chunk_texts)} چانک ساخته شد.") |
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return chunk_texts, chunk_embeddings, embeddings_model |
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chunk_texts, chunk_embeddings, embeddings_model = load_chunks_and_embeddings() |
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llm = 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 answer_from_pdf(question): |
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question_embedding = embeddings_model.embed_query(question) |
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similarities = cosine_similarity( |
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[question_embedding], |
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chunk_embeddings |
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) |
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best_idx = np.argmax(similarities) |
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best_chunk = chunk_texts[best_idx] |
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prompt = f"""بر اساس متن زیر فقط به زبان فارسی پاسخ بده: |
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متن: |
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{best_chunk} |
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سوال: |
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{question} |
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پاسخ:""" |
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response = llm.invoke(prompt) |
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return response.content |
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st.title('📚 چت با PDF') |
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if 'messages' not in st.session_state: |
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st.session_state.messages = [] |
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if 'pending_prompt' not in st.session_state: |
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st.session_state.pending_prompt = None |
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for msg in st.session_state.messages: |
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with st.chat_message(msg['role']): |
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st.markdown(f"🗨️ {msg['content']}", unsafe_allow_html=True) |
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prompt = st.chat_input("سوال خود را وارد کنید...") |
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if prompt: |
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st.session_state.messages.append({'role': 'user', 'content': prompt}) |
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st.session_state.pending_prompt = prompt |
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st.rerun() |
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if st.session_state.pending_prompt: |
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with st.chat_message('ai'): |
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thinking = st.empty() |
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thinking.markdown("🤖 در حال پردازش...") |
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response = answer_from_pdf(st.session_state.pending_prompt) |
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answer = response.strip() |
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if not answer: |
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answer = "متاسفم، اطلاعات دقیقی در این مورد ندارم." |
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thinking.empty() |
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full_response = "" |
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placeholder = st.empty() |
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for word in answer.split(): |
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full_response += word + " " |
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placeholder.markdown(full_response + "▌") |
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time.sleep(0.03) |
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placeholder.markdown(full_response) |
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st.session_state.messages.append({'role': 'ai', 'content': full_response}) |
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st.session_state.pending_prompt = None |
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