Update app.py

app.py

@@ -26,19 +26,20 @@ def predict(image, max_length=64, num_beams=4):
     preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
     pred = post_process(preds[0])
     return pred
-model_name_or_path = "deepklarity/poster2plot"
+    
+model_path = "team-indain-image-caption/hindi-image-captioning"
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 # Load model.
 model = VisionEncoderDecoderModel.from_pretrained(model_name_or_path)
 model.to(device)
 print("Loaded model")
-feature_extractor = AutoFeatureExtractor.from_pretrained(model.encoder.name_or_path)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_path)
 print("Loaded feature_extractor")
-tokenizer = AutoTokenizer.from_pretrained(model.decoder.name_or_path, use_fast=True)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
 if model.decoder.name_or_path == "gpt2":
-    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.pad_token = tokenizer.bos_token
 print("Loaded tokenizer")
-title = "Poster2Plot: Upload a Movie/T.V show poster to generate a plot"
+title = "Hindi Image Captioning"
 description = ""
 input = gr.inputs.Image(type="pil")
 example_images = sorted([f.as_posix() for f in Path("examples").glob("*.jpg")])