Delete note.py

note.py

@@ -1,104 +0,0 @@
-# -*- coding: utf-8 -*-
-"""note
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1QURIEiSXUEJNZgmvvRYhRiCHT2gYx97F
-"""
-
-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)
-    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)
-    translated_caption = translate_text(caption_text, target_lang)
-    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()
-
-
-