app.py

from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering, MarianMTModel, MarianTokenizer
import gradio as gr
import torch
import warnings

warnings.filterwarnings("ignore")

# Load BLIP models
captioning_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
captioning_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")

# Dictionary to store translation models and tokenizers for different languages
translation_models = {
    "Spanish": 'Helsinki-NLP/opus-mt-en-es',
    "German": 'Helsinki-NLP/opus-mt-en-de',
    "Chinese": 'Helsinki-NLP/opus-mt-en-zh',
    "Japanese": 'Helsinki-NLP/opus-mt-en-ja',
    "Russian": 'Helsinki-NLP/opus-mt-en-ru',
    "Arabic": 'Helsinki-NLP/opus-mt-en-ar',
    "Hindi": 'Helsinki-NLP/opus-mt-en-hi',
    "Urdu": 'Helsinki-NLP/opus-mt-en-ur'
}

# Load translation models and tokenizers
loaded_translation_models = {}
loaded_translation_tokenizers = {}

for lang, model_name in translation_models.items():
    try:
        loaded_translation_models[lang] = MarianMTModel.from_pretrained(model_name)
        loaded_translation_tokenizers[lang] = MarianTokenizer.from_pretrained(model_name)
        print(f"Successfully loaded translation model for {lang}")
    except Exception as e:
        print(f"Error loading model for {lang}: {e}")

# Captioning function
def caption(image):
    image = image.convert("RGB")
    inputs = captioning_processor(image, return_tensors="pt")
    out = captioning_model.generate(**inputs)
    return captioning_processor.decode(out[0], skip_special_tokens=True)

# Visual Question Answering function
def qna(image, question):
    image = image.convert("RGB")
    inputs = processor(image, question, return_tensors="pt")
    out = model.generate(**inputs)
    return processor.decode(out[0], skip_special_tokens=True)

# Translation function
def translate_text(text, target_lang="Spanish"):
    model = loaded_translation_models.get(target_lang)
    tokenizer = loaded_translation_tokenizers.get(target_lang)
    if model is None or tokenizer is None:
        return f"Translation model for {target_lang} is not available."
    inputs = tokenizer(text, return_tensors="pt")
    translated = model.generate(**inputs)
    return tokenizer.decode(translated[0], skip_special_tokens=True)

# Combined Captioning and Translation function
def caption_and_translate(image, target_lang="Spanish"):
    caption_text = caption(image)
    print(f"Generated caption: {caption_text}")
    translated_caption = translate_text(caption_text, target_lang)
    print(f"Translated caption: {translated_caption}")
    return caption_text, translated_caption

# Create Gradio interfaces
interface1 = gr.Interface(fn=caption,
                          inputs=gr.components.Image(type="pil"),
                          outputs=gr.components.Textbox(label="Generated Caption by BLIP"),
                          description="BLIP Image Captioning")

interface2 = gr.Interface(fn=qna,
                          inputs=[gr.components.Image(type="pil"), gr.components.Textbox(label="Question")],
                          outputs=gr.components.Textbox(label="Answer generated by BLIP"),
                          description="BLIP Visual Question Answering of Images")

interface3 = gr.Interface(fn=caption_and_translate,
                          inputs=[gr.components.Image(type="pil"), gr.components.Dropdown(label="Target Language", choices=["Spanish", "German", "Chinese", "Japanese", "Russian", "Arabic", "Hindi", "Urdu"])],
                          outputs=[gr.components.Textbox(label="Generated Caption"),
                                   gr.components.Textbox(label="Translated Caption")],
                          description="Image Captioning and Translation")

title = "Automated Image Captioning and Visual QnA Engine"

final_interface = gr.TabbedInterface([interface1, interface2, interface3],
                                     ["Captioning", "Visual QnA", "Captioning and Translation"],
                                     title=title, theme=gr.themes.Soft())

final_interface.launch(inbrowser=True)