Update app.py

app.py

@@ -6,43 +6,74 @@ import torch
 
 from Model import TRCaptionNet, clip_transform
 
-model_ckpt = "./checkpoints/TRCaptionNet_L14_berturk_tasviret.pth"
 
-# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-device = "cpu"
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+# device = "cpu"
+
+preprocess_tasviret = clip_transform(336)
+model_tasviret = TRCaptionNet({
+    "max_length": 35,
+    "clip": "ViT-L/14@336px",
+    "bert": "dbmdz/bert-base-turkish-cased",
+    "proj": True,
+    "proj_num_head": 16
+})
+model_ckpt = "./checkpoints/TRCaptionNet-TasvirEt_L14_334_berturk.pth"
+model_tasviret.load_state_dict(torch.load(model_ckpt, map_location=device)["model"], strict=True)
+model_tasviret = model_tasviret.to(device)
+model_tasviret.eval()
 
 preprocess = clip_transform(224)
 model = TRCaptionNet({
     "max_length": 35,
     "clip": "ViT-L/14",
-    "bert": "bert.json",
+    "bert": "dbmdz/bert-base-turkish-cased",
     "proj": True,
     "proj_num_head": 16
 })
+model_ckpt = "./checkpoints/TRCaptionNet_L14_berturk.pth"
 model.load_state_dict(torch.load(model_ckpt, map_location=device)["model"], strict=True)
 model = model.to(device)
 model.eval()
 
 
+
 def inference(raw_image, min_length, repetition_penalty):
+    batch = preprocess_tasviret(raw_image).unsqueeze(0).to(device)
+    caption_tasviret = model_tasviret.generate(batch, min_length=min_length, repetition_penalty=repetition_penalty)[0]
+    
     batch = preprocess(raw_image).unsqueeze(0).to(device)
     caption = model.generate(batch, min_length=min_length, repetition_penalty=repetition_penalty)[0]
-    return caption
+    
+    return [caption, caption_tasviret]
 
 
 inputs = [gr.Image(type='pil', interactive=True,),
-          gr.Slider(minimum=6, maximum=22, value=11, label="MINIMUM CAPTION LENGTH", step=1),
+          gr.Slider(minimum=4, maximum=22, value=8, label="MINIMUM CAPTION LENGTH", step=1),
           gr.Slider(minimum=1, maximum=2, value=1.6, label="REPETITION PENALTY")]
-outputs = gr.components.Textbox(label="Caption")
-title = "TRCaptionNet"
+          
+outputs = [gr.components.Textbox(label="Caption"), gr.components.Textbox(label="Caption-TasvirEt")]
+title = "TRCaptionNet-TasvirEt"
 paper_link = ""
 github_link = "https://github.com/serdaryildiz/TRCaptionNet"
-description = f"<p style='text-align: center'><a href='{github_link}' target='_blank'>TRCaptionNet</a> : A novel and accurate deep Turkish image captioning model with vision transformer based image encoders and deep linguistic text decoders"
+IEEE_link = "https://github.com/serdaryildiz/TRCaptionNet"
+
+description = f"<p style='text-align: center'><a href='{IEEE_link}' target='_blank'> SIU2024: Turkish Image Captioning with Vision Transformer Based Encoders and Text Decoders</a> "
+description += f"<p style='text-align: center'><a href='{github_link}' target='_blank'>TRCaptionNet</a> : A novel and accurate deep Turkish image captioning model with vision transformer based image encoders and deep linguistic text decoders"
+
 examples = [
     ["images/test1.jpg"],
     ["images/test2.jpg"],
     ["images/test3.jpg"],
-    ["images/test4.jpg"]
+    ["images/test4.jpg"],
+    ["images/test5.jpg"],
+    ["images/test6.jpg"],
+    ["images/test7.jpg"],
+    ["images/test8.jpg"],
+    ["images/test9.jpg"],
+    ["images/test10.jpg"],
+    ["images/test11.jpg"],
 ]
 article = f"<p style='text-align: center'><a href='{paper_link}' target='_blank'>Paper</a> | <a href='{github_link}' target='_blank'>Github Repo</a></p>"
 css = ".output-image, .input-image, .image-preview {height: 600px !important}"
@@ -56,4 +87,3 @@ iface = gr.Interface(fn=inference,
                      article=article,
                      css=css)
 iface.launch()
-