Create app.py

app.py

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+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;'>智悠科技大语言模型</span> - 智能漂浮舱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()