add model

app.py

@@ -1,7 +1,53 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# make function using import pip to install torch
+import pip
+pip.main(['install', 'torch'])
+pip.main(['install', 'transformers'])
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+import torch
+import transformers
+
+
+# saved_model
+def load_model(model_path):
+    saved_data = torch.load(
+        model_path,
+        map_location="cpu"
+    )
+
+    bart_best = saved_data["model"]
+    train_config = saved_data["config"]
+    tokenizer = transformers.PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')
+
+    ## Load weights.
+    model = transformers.BartForConditionalGeneration.from_pretrained('gogamza/kobart-base-v1')
+    model.load_state_dict(bart_best)
+
+    return model, tokenizer
+
+
+# main
+def inference(prompt):
+    model_path = "./kobart-model-summary.pth"
+
+    model, tokenizer = load_model(
+        model_path=model_path
+        )
+
+    input_ids = tokenizer.encode(prompt)
+    input_ids = torch.tensor(input_ids)
+    input_ids = input_ids.unsqueeze(0)
+    output = model.generate(input_ids)
+    output = tokenizer.decode(output[0], skip_special_tokens=True)    
+
+    return output
+
+
+demo = gr.Interface(
+    fn=inference, 
+    inputs="text", 
+    outputs="text" #return 값
+    ).launch() # launch(share=True)를 설정하면 외부에서 접속 가능한 링크가 생성됨
+
+demo.launch()

kobart-model-summary.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:533e88edefe7f9621fb13ac6df3e7b288afe876218c8bf9468900f04014a8857
+size 496661433