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# app.py
import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration
from gtts import gTTS
import io
from PIL import Image
# -------------------------------
# Load BLIP-base model (lighter version)
# -------------------------------
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
# -------------------------------
# Generate caption function
# -------------------------------
def generate_caption_fn(image):
# Convert uploaded image to PIL
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
# BLIP preprocessing
inputs = processor(images=image, return_tensors="pt")
# Generate caption
out = model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
return caption
# -------------------------------
# Convert text to speech using gTTS
# -------------------------------
def text_to_speech(caption):
tts = gTTS(text=caption, lang='en')
mp3_fp = io.BytesIO()
tts.write_to_fp(mp3_fp)
mp3_fp.seek(0)
return mp3_fp
# -------------------------------
# Gradio interface: Caption + Audio
# -------------------------------
def generate_caption_tts(image):
caption = generate_caption_fn(image)
audio = text_to_speech(caption)
return caption, audio
interface = gr.Interface(
fn=generate_caption_tts,
inputs=gr.Image(type="numpy"),
outputs=[gr.Textbox(label="Generated Caption"), gr.Audio(type="file", label="TTS Audio")],
title="Blind Assistant: Image Captioning",
description="Upload an image and get a descriptive caption + speech."
)
interface.launch()
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