app.py

import gradio as gr
from transformers import BlipForConditionalGeneration, BlipProcessor
import torch
import tempfile
from gtts import gTTS

# Load models
device = "cpu"
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model_image_captioning = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)

def generate_caption_tts(image):

  inputs = processor(images=image, return_tensors="pt")
  inputs["max_length"] = 20
  inputs["num_beams"] = 5
  outputs = model_image_captioning.generate(**inputs)

  caption = processor.batch_decode(outputs, skip_special_tokens=True)[0]

  speech = gTTS(caption, lang="en")
  tmp_file = tempfile.mkstemp()[1]
  speech.save(tmp_file)

  return (caption, tmp_file)


title ="<span style='font-style: italic; font-weight: bold; color: darkred;'>模多多AI科技大语言模型</span> - PTI多模态健康交互机器人"

description = "BLPM模型：引导性语言图像预训练以实现统一视觉语言理解和生成。 请上传您的图像（或自动感知您的状况）"

iface = gr.Interface(
  fn=generate_caption_tts,
  title=title,
  description=description,
  inputs=gr.inputs.Image(shape=(224,224)),
  outputs=["text", "audio"]
)


#iface.launch(share=True, debug=True)
iface.launch()