Create app.py

app.py

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+import gradio as gr
+import mimetypes
+import pdfminer.high_level
+from transformers import pipeline
+
+classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def load_content(file, text):
+    if text.strip():
+        return text
+    if file is None:
+        return ""
+    mime = mimetypes.guess_type(file.name)[0]
+    binary = file.read()
+    if mime and "pdf" in mime:
+        return pdfminer.high_level.extract_text(file)
+    return binary.decode("utf-8", errors="ignore")
+
+def detect_sentiment(file, text):
+    content = load_content(file, text).strip()
+    if not content:
+        return "Нет текста"
+    return f"Тональность: {classifier(content)[0]['label']}"
+
+def summarize_text(file, text):
+    content = load_content(file, text).strip()
+    if not content:
+        return "Нет текста"
+    return summarizer(content, max_length=65, min_length=25, do_sample=False)[0]['summary_text']
+
+def full_analysis(file, text):
+    content = load_content(file, text).strip()
+    if not content:
+        return "Нет текста", "Нет текста"
+    sentiment = f"Тональность: {classifier(content)[0]['label']}"
+    summary = summarizer(content, max_length=65, min_length=25, do_sample=False)[0]['summary_text']
+    return sentiment, summary
+
+def reset_fields():
+    return "", None, "", ""
+
+with gr.Blocks() as demo:
+    gr.Markdown("## ReviewSmart — анализ отзывов с помощью NLP")
+
+    with gr.Row():
+        input_text = gr.Textbox(label="Текст отзыва", lines=8, placeholder="Введите или загрузите отзыв...")
+        input_file = gr.File(label="Файл (.pdf, .txt)", file_types=[".pdf", ".txt"])
+
+    with gr.Row():
+        btn_sentiment = gr.Button("Определить тональность")
+        btn_summary = gr.Button("Создать резюме")
+        btn_both = gr.Button("Анализировать оба")
+        btn_clear = gr.Button("Очистить")
+
+    with gr.Row():
+        sentiment_box = gr.Textbox(label="Результат анализа тональности", lines=2)
+        summary_box = gr.Textbox(label="Результат резюмирования", lines=4)
+
+    btn_sentiment.click(fn=detect_sentiment, inputs=[input_file, input_text], outputs=sentiment_box)
+    btn_summary.click(fn=summarize_text, inputs=[input_file, input_text], outputs=summary_box)
+    btn_both.click(fn=full_analysis, inputs=[input_file, input_text], outputs=[sentiment_box, summary_box])
+    btn_clear.click(fn=reset_fields, outputs=[input_text, input_file, sentiment_box, summary_box])
+
+demo.launch()