Create app.py

app.py

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+import gradio as gr
+from transformers import BlipProcessor, BlipForConditionalGeneration, MarianMTModel, MarianTokenizer
+from PIL import Image
+import torch
+
+# Load translation model
+translator_model_ar = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
+translator_tokenizer_ar = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
+
+# Load BLIP model (fine-tuned)
+model_path = "saja003/MuniVis"
+processor_en = BlipProcessor.from_pretrained(model_path)
+model_en = BlipForConditionalGeneration.from_pretrained(model_path)
+model_en.eval()
+
+# Function to describe image
+def describe_image(image, language):
+    if language == "Arabic":
+        inputs = processor_en(image, return_tensors="pt")
+        with torch.no_grad():
+            out = model_en.generate(**inputs)
+        description = processor_en.decode(out[0], skip_special_tokens=True)
+        inputs_ar = translator_tokenizer_ar(description, return_tensors="pt")
+        with torch.no_grad():
+            translated_tokens = translator_model_ar.generate(**inputs_ar)
+        arabic_description = translator_tokenizer_ar.decode(translated_tokens[0], skip_special_tokens=True)
+        return arabic_description
+
+    elif language == "English":
+        inputs_en = processor_en(image, return_tensors="pt")
+        with torch.no_grad():
+            out_en = model_en.generate(**inputs_en)
+        description_en = processor_en.decode(out_en[0], skip_special_tokens=True)
+        return description_en
+
+# Gradio UI
+iface = gr.Interface(
+    fn=describe_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload an Image"),
+        gr.Dropdown(choices=["Arabic", "English"], label="Select Language")
+    ],
+    outputs="text",
+    title="Image Captioning with Arabic Translation",
+    description="Select the language and upload an image to get a description."
+)
+
+if __name__ == "__main__":
+    iface.launch()