Update app.py

app.py

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+import gradio as gr
+import easyocr
+import transformer
+
+reader=easyocr.Reader(['en'])
+# this needs to run only once to load the model into memory
+result=reader.readtext('/content/WhatsApp Image 2023-09-23 at 8.03.28 AM.jpeg',detail=0)
+news=" ".join(result)
+from transformers import AutoTokenizer
+tokenizer=AutoTokenizer.from_pretrained("facebook/mbart-large-50-one-to-many-mmt",use_fast=False)
+from transformers import MBartForConditionalGeneration
+# download and save model
+model=MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt")
+input_text=[news]
+# convert sentences to tensors
+model_inputs=tokenizer(input_text,return_tensors="pt",padding=True,truncation=True)
+# translate from English to Hindi
+generated_tokens=model.generate(
+**model_inputs,
+forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"]
+)
+translation=tokenizer.batch_decode(generated_tokens,skip_special_tokens=True)
+translation
+
+from transformers import AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-one-to-many-mmt", use_fast=False)
+
+from transformers import MBartForConditionalGeneration
+
+# download and save model
+model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt")
+
+def translator(img):
+
+    reader = easyocr.Reader(['en'])
+    result = reader.readtext(img,detail = 0)
+    news= " ".join(result)
+    input_text = [news]
+
+      # convert sentences to tensors
+    model_inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
+
+
+    # translate from English to Hindi
+    generated_tokens = model.generate(
+        **model_inputs,
+        forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"]
+    )
+
+    translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+
+    return translation
+
+demo = gr.Interface(fn=translator, inputs=gr.Image(), outputs="text")
+
+demo.launch(inline=False)