setup website

app.py

@@ -0,0 +1,34 @@
+import os
+from flask import Flask, request, render_template, send_file, jsonify
+import io
+from functions import (
+    sentiment_analysis
+)
+import os
+
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'uploads'
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+
+@app.route('/', methods = ['GET', 'POST'])
+def index():
+    result_text = None
+    result_image = None
+    error = None
+
+    if request.method == 'POST':
+        action = request.form.get('action')
+
+        if action == 'sentiment':
+            text = request.form.get('text', '')
+            if text:
+                result_text = sentiment_analysis(text)
+            else:
+                error = "Введите текст"
+
+
+    return render_template('index.html', result_text=result_text, error=error)
+
+if __name__=='__main__':
+    app.run(debug=True, host='0.0.0.0', port=5000)

config.py

@@ -8,15 +8,8 @@ load_dotenv()
 
 BOT_TOKEN = os.getenv("BOT_TOKEN")
 HF_TOKEN = os.getenv("HF_TOKEN")
-# REPLICATE_API_TOKEN = os.getenv("REPLICATE_API_TOKEN")
-
 
 SENTIMENT_API = "https://huggingface.co/tabularisai/multilingual-sentiment-analysis"
 model_name = "tabularisai/multilingual-sentiment-analysis"
-# OBJECT_DETECTION_API = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50"
-# TEXT_GEN_API = "https://api-inference.huggingface.co/models/gpt2"
-# SUMMARIZATION_API = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
-# IMAGE_COLORIZATION_API = "https://api-inference.huggingface.co/models/johnj/colorization"
-# EMOTION_API = "https://api-inference.huggingface.co/models/harsh3474/face-emotion-recognition"
 
 headers = {"Authorization": f"Bearer {HF_TOKEN}"}

database.py

@@ -1,36 +0,0 @@
-import sqlite3
-import datetime
-
-DB_NAME= "history.db"
-
-def init_db():
-    conn=sqlite3.connect(DB_NAME)
-    c=conn.cursor()
-    c.execute('''CREATE TABLE IF NOT EXISTS history
-                (id INTEGER PRIMARY KEY AUTOINCREMENT,
-                user_id INTEGER,
-                command TEXT,
-                input TEXT,
-                result TEXT,
-                timestamp TEXT)
-                ''')
-    conn.commit()
-    conn.close()
-
-def add_record(user_id, command, input_data, result):
-    conn=sqlite3.connect(DB_NAME)
-    c = conn.cursor()
-    timestamp = datetime.datetime.now().isoformat()
-    c.execute("INSERT INTO history (user_id, command, input, result, timestamp) VALUES (?, ?, ?, ?, ?)",
-        (user_id, command, input_data, result, timestamp))
-    conn.commit()
-    conn.close()
-
-def get_history(user_id, limit=10):
-    conn = sqlite3.connect(DB_NAME)
-    c= conn.cursor()
-    c.execute("SELECT command, input, result, timestamp FROM history WHERE user_id = ? ORDER BY time stamp DESC LIMIT ?",
-              (user_id, limit))
-    rows = c.fetchall()
-    conn.close()
-    return rows

functions/sentiment.py

@@ -4,32 +4,10 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
 
-# import os
-# # Отключаем все прокси-переменные
-# os.environ.pop('HTTP_PROXY', None)
-# os.environ.pop('HTTPS_PROXY', None)
-# os.environ.pop('http_proxy', None)
-# os.environ.pop('https_proxy', None)
-# os.environ.pop('ALL_PROXY', None)
-
-# async def sentiment_analysis(text:str)->str:
-#     payload = {"inputs":text}
-#     try:
-#         response = requests.post(SENTIMENT_API, headers=headers, json=payload)
-#         result = response.json()
-#         if isinstance(result, list) and len(result)>0:
-#             labels = result[0]
-#             best = max(labels, key=lambda x: x['score'])
-#             label_map = {"LABEL_0":"негативный", "LABEL_1": "нейтральный", "LABEL_2": "позитивный"}
-#             return label_map.get(best['label'], best['label'])
-#         return "не удалось определить"
-#     except Exception as e:
-#         return f"Error: {str(e)}"
-
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-async def sentiment_analysis(text)->str:
+def sentiment_analysis(text)->str:
     '''принимает строку, возвращает тональность'''
     try:
         inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
@@ -37,9 +15,9 @@ async def sentiment_analysis(text)->str:
             outputs = model(**inputs)
         #вероятности классов
         probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        pred_class = torch.argmax(probabilities, dim=-1).tolist()
+        pred_class = torch.argmax(probabilities, dim=-1).item()
 
-        if pred_class <=1:
+        if pred_class <= 1:
             return "Негативный"
         elif pred_class ==2:
             return "Нейтральный"
@@ -48,12 +26,3 @@ async def sentiment_analysis(text)->str:
 
     except Exception as e:
         return f"Error: {str(e)}"
-#
-#
-# texts = [
-#     "I absolutely love the new design of this app!", "The customer service was disappointing.", "The weather is fine, nothing special.",
-#     "Я в восторге от этого нового гаджета!", "Этот сервис оставил у меня только разочарование.", "Встреча была обычной, ничего особенного.",
-#     ]
-#
-# for text, sentiment in zip(texts, sentiment_analysis(texts)):
-#     print(f"Text: {text}\nSentiment: {sentiment}\n")

handlers/__init__.py

@@ -1,10 +0,0 @@
-from aiogram import Router
-from .sentiment import router as sentiment_router
-from .history import router as history_router
-from .start import router as start_router
-
-
-def register_all_handlers(dp:Router):
-    dp.include_router(start_router)
-    dp.include_router(sentiment_router)
-    dp.include_router(history_router)

handlers/history.py

@@ -1,18 +0,0 @@
-from aiogram import Router
-from aiogram.types import Message
-from aiogram.filters import Command
-from database import get_history
-
-
-router = Router()
-
-@router.message("history")
-async def cmd_history(message:Message):
-    rows = get_history(message.from_user.id)
-    if not rows:
-        await message.answer("история пуста")
-        return
-    answer = "Последние 10 действий:\n"
-    for cmd, inp, res, ts in rows:
-        answer+= f"- {ts[:16]} | {cmd} | {inp[:15]} \n"
-    await message.answer(answer[:20])

handlers/sentiment.py

@@ -1,23 +0,0 @@
-from aiogram.types import Message
-from aiogram.filters import Command
-from aiogram import Router, F
-from aiogram.fsm.context import FSMContext
-from functions import sentiment_analysis
-from database import add_record
-from states import MemeState
-
-
-# для связывания URL-адресов с кодом
-router = Router()
-
-
-@router.message(Command("sentiment"))
-async def cmd_sentiment(message:Message, state: FSMContext):
-    await state.set_state(MemeState.waiting_text)
-    await message.answer("Напишите текст для анализа")
-
-@router.message(MemeState.waiting_text, F.text)
-async def analyze(msg:Message, state: FSMContext):
-    result = await sentiment_analysis(msg.text)
-    await msg.answer(f"Тональность {result}")
-    add_record(msg.from_user.id, "sentiment", msg.text[:500], result)

handlers/start.py

@@ -1,15 +0,0 @@
-from aiogram import Router
-from aiogram.types import Message
-from aiogram.filters import Command
-
-router = Router()
-
-@router.message(Command("start"))
-async def start(message:Message):
-    text =("Hello! Я нейросетевой бот.\n"
-        "Мои команды:\n"
-        "/start\n"
-        "/help\n"
-        "/sentiment\n"
-        "/history\n")
-    await message.answer(text)

main.py

@@ -1,34 +0,0 @@
-import asyncio
-import logging
-from aiogram import Bot, Dispatcher
-from aiogram.filters import Command
-from aiogram.types import Message
-from config import BOT_TOKEN
-from database import init_db
-from handlers import register_all_handlers
-
-
-# @dp.message(F.text)
-# async def echo(message:Message):
-#     await message.answer(f"{message.text}")
-#
-# @dp.message(F.sticker)
-# async def echo_sticker(message: Message):
-#     await message.answer_sticker(sticker=message.sticker.file_id)
-#
-# @dp.message(F.photo)
-# async def echo_photo(message:Message):
-#     photo = message.photo[-1]
-#     await message.answer_photo(photo.file_id, caption=message.caption)
-
-async def main():
-    init_db()
-    bot = Bot(token=BOT_TOKEN)
-    dp = Dispatcher()
-    register_all_handlers(dp)
-    logging.basicConfig(level=logging.INFO)
-    await dp.start_polling(bot)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

requirements.txt

@@ -0,0 +1,4 @@
+flask
+torch
+transformers
+dotenv

states.py

@@ -1,9 +0,0 @@
-from aiogram.fsm.state import StatesGroup, State
-
-
-class StyleTransferStates(StatesGroup):
-    waiting_content = State()
-    waiting_style = State()
-
-class MemeState(StatesGroup):
-    waiting_text=State()

templates/index.html

@@ -0,0 +1,41 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Нейросетевой помощник</title>
+    <style>
+        body { font-family: Arial; margin:40; }
+        .function-box { border: 1px solid #ccc; padding: 15px; margin-bottom:20px; border-radius: 8px; }
+        .result { background: #f0f0f0; padding: 10px; border-radius: 5px; margin-top: 10px;}
+        .error {color: red;}
+    </style>
+</head>
+<body>
+    <h1>Нейросетевой помощник</h1>
+    <p>Выберите функцию и введите данные</p>
+
+    {% if error %}
+        <div class="error"{{ error }}</div>
+    {% endif %}
+
+    <form method="POST" enctype="multipart/form-data">
+        <div class="function-box">
+            <h3>Текстовые функции</h3>
+            <select name="action">
+                <option value="sentiment">Анализ тональности</option>
+            </select>
+            <br><br>
+            <textarea name="text" rows="4" cols="50" placeholder="Введите текст..."></textarea>
+            <br>
+            <input type="submit" value="Отправить">
+        </div>
+    </form>
+
+    {% if result_text %}
+        <div class="result">
+            <h3>Результат</h3>
+            <p>{{ result_text }}</p>
+        </div>
+    {% endif %}
+</body>
+</html>