Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,171 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+
+
 import streamlit as st
+import pandas as pd
+import numpy as np
+from datasets import load_dataset, concatenate_datasets
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+import spacy
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+import re
+from bs4 import BeautifulSoup
+
+# === Загрузка и подготовка данных ===
+
+
+spacy.cli.download('ru_core_news_lg')
+nltk.download('punkt_tab', download_dir='/usr/local/share/nltk_data')
+nltk.download('stopwords')
+
+
+@st.cache_resource
+def load_data():
+    # Загрузка датасета
+    data = load_dataset('Romyx/ru_QA_school_history', split='train')
+    df = pd.DataFrame(data)
+    df['Pt_question'] = df['question'].apply(preprocess_text)
+    df['Pt_answer'] = df['answer'].apply(preprocess_text)
+    return df
+
+@st.cache_resource
+def load_model_and_tokenizer():
+    # Загрузка предобученной модели вопрос-ответа (например, SberQuad)
+    model_name = "AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru"  # замените на нужную модель, например, "bert-base-uncased"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+    return tokenizer, model
+
+@st.cache_resource
+def build_vectorizer(_df):
+    combined_texts = _df['Pt_question'].tolist() + _df['Pt_answer'].tolist()
+    vectorizer = TfidfVectorizer()
+    tfidf_matrix = vectorizer.fit_transform(combined_texts)
+    return vectorizer, tfidf_matrix
+
+# === Предобработка текста ===
+
+# Загрузка Spacy модели
+nlp = spacy.load('ru_core_news_lg')
+stop_words = set(stopwords.words('russian'))
+
+cache_dict = {}
+
+def get_norm_form(word):
+    if word in cache_dict:
+        return cache_dict[word]
+    norm_form = nlp(word)[0].lemma_
+    cache_dict[word] = norm_form
+    return norm_form
+
+def remove_html_tags(text):
+    soup = BeautifulSoup(text, 'html.parser')
+    return soup.text
+
+def preprocess_text(text):
+    if pd.isna(text) or text is None:
+        return ""
+    text = remove_html_tags(text)
+    text = text.lower()
+
+    # Обработка знаков препинания
+    text = re.sub(r'([^\w\s-]|_)', r' \1 ', text)
+    text = re.sub(r'\s+', ' ', text)
+    text = re.sub(r'(\w+)-(\w+)', r'\1 \2', text)
+    text = re.sub(r'(\d+)(г|кг|см|м|мм|л|мл)', r'\1 \2', text)
+
+    # Удаление всего, кроме букв, цифр и пробелов
+    text = re.sub(r'[^\w\s]', '', text)
+
+    tokens = word_tokenize(text)
+    tokens = [token for token in tokens if token not in stop_words]
+    tokens = [get_norm_form(token) for token in tokens]
+
+    words_to_remove = {"ответ", "new"}
+    tokens = [token for token in tokens if token not in words_to_remove]
+
+    return ' '.join(tokens)
+
+# === Основная функция получения ответа ===
+def get_answer_from_qa_model(user_question, df, vectorizer, tfidf_matrix, model, tokenizer):
+    processed = preprocess_text(user_question)
+    user_vec = vectorizer.transform([processed])
+
+    similarities = cosine_similarity(user_vec, tfidf_matrix).flatten()
+
+    # Проверка, что similarities не пустой
+    if len(similarities) == 0:
+        return "Тема не входит в программу этих классов."
+
+    best_match_idx = similarities.argmax()
+    best_score = similarities[best_match_idx]
+
+    if best_score > 0.1:
+        # Проверка, что индекс не выходит за границы
+        if best_match_idx >= len(df):
+            return "Тема не входит в программу этих классов."
+
+        context = df.iloc[best_match_idx]['answer']
+        question = user_question
+
+        inputs = tokenizer(question, context, return_tensors="pt", truncation=True, padding=True)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        start_scores = outputs.start_logits
+        end_scores = outputs.end_logits
+
+        # Проверка на корректность размера логитов
+        if len(start_scores.shape) == 2:
+            start_idx = torch.argmax(start_scores, dim=1)[0].item()
+            end_idx = torch.argmax(end_scores, dim=1)[0].item()
+        else:
+            start_idx = torch.argmax(start_scores).item()
+            end_idx = torch.argmax(end_scores).item()
+
+        # Проверка, что индексы не выходят за пределы
+        seq_len = inputs['input_ids'].shape[1]
+        if start_idx >= seq_len or end_idx >= seq_len or start_idx > end_idx:
+            return "Ответ не найден."
+
+        answer = tokenizer.decode(inputs['input_ids'][0][start_idx:end_idx+1], skip_special_tokens=True)
+    else:
+        answer = "Извините, я не понимаю вопрос."
+
+    return answer
+
+# === Интерфейс Streamlit ===
+
+def main():
+    st.title("🤖 ИИ-ассистент по истории (на основе вопрос-ответа)")
+
+    st.write("Задайте вопрос, и я постараюсь найти на него ответ из базы.")
+
+    # Загрузка данных и модели
+    df = load_data()
+    tokenizer, model = load_model_and_tokenizer()
+    vectorizer, tfidf_matrix = build_vectorizer(df)
+
+    # Поле ввода вопроса
+    user_input = st.text_input("Введите ваш вопрос:")
+
+    if st.button("Получить ответ"):
+        if user_input.strip():
+            with st.spinner("Ищем ответ..."):
+                response = get_answer_from_qa_model(
+                    user_input, df, vectorizer, tfidf_matrix, model, tokenizer
+                )
+            st.success("Ответ:")
+            st.write(response)
+        else:
+            st.warning("Пожалуйста, введите вопрос.")
+
+if __name__ == "__main__":
+    main()
+
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))