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app.py

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+import gradio as gr
+import torch
+from PIL import Image
+from transformers import (
+    BlipProcessor,
+    BlipForConditionalGeneration,
+    AutoTokenizer,
+    AutoModelForSeq2SeqLM
+)
+from typing import Union
+from gtts import gTTS
+import os
+
+class ImageCaptionPipeline:
+    def __init__(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+        self.blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(self.device)
+        self.translator_tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ru")
+        self.translator_model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-ru").to(self.device)
+
+    def generate_caption(self, image: Union[str, Image.Image], language: str = "Русский") -> str:
+        if isinstance(image, str):
+            image = Image.open(image)
+        image = image.convert("RGB")
+        inputs = self.blip_processor(images=image, return_tensors="pt").to(self.device)
+        with torch.no_grad():
+            output_ids = self.blip_model.generate(**inputs, max_length=200, num_beams=4)
+            english_caption = self.blip_processor.decode(output_ids[0], skip_special_tokens=True)
+        if language == "Русский":
+            translated_inputs = self.translator_tokenizer(english_caption, return_tensors="pt", padding=True).to(self.device)
+            with torch.no_grad():
+                translated_ids = self.translator_model.generate(**translated_inputs, max_length=200, num_beams=4)
+                russian_caption = self.translator_tokenizer.decode(translated_ids[0], skip_special_tokens=True)
+            return russian_caption
+        return english_caption
+
+def app(image: Image.Image, language: str) -> tuple:
+    if image is not None:
+        pipeline = ImageCaptionPipeline()
+        caption = pipeline.generate_caption(image, language=language)
+        lang_code = "ru" if language == "Русский" else "en"
+        tts = gTTS(text=caption, lang=lang_code)
+        audio_path = "caption_audio.mp3"
+        tts.save(audio_path)
+        return caption, audio_path
+    return "Загрузите изображение и выберите язык для получения подписи.", None
+
+with gr.Blocks() as iface:
+    gr.Markdown("# Генератор подписей")
+    gr.Markdown("Загрузите изображение и выберите язык.")
+    language = gr.Dropdown(choices=["Русский", "English"], label="Язык", value="Русский")
+    image = gr.Image(type="pil", label="Изображение", height=400, width=400)
+    submit_button = gr.Button("Сгенерировать", elem_classes="btn")
+    caption_output = gr.Textbox(label="Подпись")
+    audio_output = gr.Audio(label="Озвучка")
+
+    submit_button.click(
+        fn=app,
+        inputs=[image, language],
+        outputs=[caption_output, audio_output]
+    )
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+torch
+transformers
+pillow
+gtts
+sacremoses