Create app.py

app.py

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+import gradio as gr
+import tempfile
+from transformers import  MT5ForConditionalGeneration, MT5Tokenizer,ViltProcessor, ViltForQuestionAnswering, AutoTokenizer
+import torch
+from PIL import Image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# English to Persian model
+fa_en_translation_tokenizer = MT5Tokenizer.from_pretrained("SeyedAli/Persian-to-English-Translation-mT5-V1").to(device)
+fa_en_translation_model = MT5ForConditionalGeneration.from_pretrained("SeyedAli/Persian-to-English-Translation-mT5-V1").to(device)
+
+def run_fa_en_transaltion_model(input_string, **generator_args):
+    input_ids = fa_en_translation_tokenizer.encode(input_string, return_tensors="pt")
+    res = fa_en_translation_model.generate(input_ids, **generator_args)
+    output = fa_en_translation_tokenizer.batch_decode(res, skip_special_tokens=True)
+    return output
+
+# Persian to English model
+en_fa_translation_tokenizer = MT5Tokenizer.from_pretrained("SeyedAli/English-to-Persian-Translation-mT5-V1").to(device)
+en_fa_translation_model = MT5ForConditionalGeneration.from_pretrained("SeyedAli/English-to-Persian-Translation-mT5-V1").to(device)
+
+def run_en_fa_transaltion_model(input_string, **generator_args):
+    input_ids = en_fa_translation_tokenizer.encode(input_string, return_tensors="pt")
+    res = en_fa_translation_model.generate(input_ids, **generator_args)
+    output = en_fa_translation_tokenizer.batch_decode(res, skip_special_tokens=True)
+    return output
+
+# Visual Question Answering model
+VQA_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa").to(device)
+VQA_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa").to(device)
+
+
+def VQA(image,text):
+    with tempfile.NamedTemporaryFile(suffix=".png") as temp_image_file:
+        # Copy the contents of the uploaded image file to the temporary file
+        Image.fromarray(image).save(temp_image_file.name)
+        # Load the image file using Pillow
+        image = Image.open(temp_image_file.name)
+        # prepare inputs
+        encoding = processor(image, run_fa_en_transaltion_model(text), return_tensors="pt")
+        # forward pass
+        outputs = model(**encoding)
+        logits = outputs.logits
+        idx = logits.argmax(-1).item()
+        output=[]
+        for item in model.config.id2label[idx]
+            output.append(run_en_fa_transaltion_model(item))
+        return output
+
+iface = gr.Interface(fn=VQA, inputs=["image","text"], outputs="text")
+iface.launch(share=False)