Create app.py

app.py

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+import gradio as gr
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_retrieval_chain
+from langchain.chat_models.gigachat import GigaChat
+from langchain_community.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+import os
+import telebot
+
+
+def get_yt_links(contexts):
+    html = '''
+    <iframe width="100%" height="200" src="{}?start={}" \
+    title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; \
+    encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" \
+    allowfullscreen></iframe>
+    '''
+    yt_htmls = []
+    for context in contexts:
+        link = context.metadata['link']
+        start = context.metadata['time']
+        yt_htmls.append(html.format(link, start))
+    return yt_htmls
+
+
+def process_input(text):
+    response = retrieval_chain.invoke({"input": text})
+    bot.send_message(int(user_id), str(response))
+    youtube_links = get_yt_links(response['context'])
+    return response['answer'], youtube_links[0], youtube_links[1], youtube_links[2]
+
+giga = os.getenv('GIGA')
+token = os.getenv('BOT')
+user_id = os.getenv('CREATOR')
+bot = telebot.TeleBot(token)
+model_name = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
+model_kwargs = {'device': 'cpu'}
+encode_kwargs = {'normalize_embeddings': False}
+embedding = HuggingFaceEmbeddings(model_name=model_name,
+                                  model_kwargs=model_kwargs,
+                                  encode_kwargs=encode_kwargs)
+
+vector_db = FAISS.load_local('faiss_index',
+                            embeddings=embedding,
+                            allow_dangerous_deserialization=True)
+llm = GigaChat(credentials=giga, verify_ssl_certs=False, profanity_check=False)
+
+prompt = ChatPromptTemplate.from_template('''Ответь на вопрос пользователя. \
+Используй при этом только информацию из контекста. Если в контексте нет \
+информации для ответа, сообщи об этом пользователю.
+Контекст: {context}
+Вопрос: {input}
+Ответ:'''
+)
+
+embedding_retriever = vector_store.as_retriever(search_kwargs={"k": 3})
+
+document_chain = create_stuff_documents_chain(
+    llm=llm,
+    prompt=prompt
+    )
+
+retrieval_chain = create_retrieval_chain(embedding_retriever, document_chain)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(label="Введите запрос")
+            submit_btn = gr.Button("Отправить запрос")
+            text_output = gr.Textbox(label="Ответ", interactive=False)
+        
+        with gr.Column():
+            youtube_video1 = gr.HTML()
+            youtube_video2 = gr.HTML()
+            youtube_video3 = gr.HTML()
+    
+    submit_btn.click(process_input, text_input, [text_output, youtube_video1, youtube_video2, youtube_video3])
+
+
+    demo.launch()