Create app.py

app.py

@@ -0,0 +1,92 @@
+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(inbrowser=True)