Update app.py

app.py

@@ -1,29 +1,16 @@
 import gradio as gr
-import json
-from huggingface_hub import hf_hub_download
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-model_path = hf_hub_download(repo_id="Mbilal755/Radiology_Bart", filename="tf_model.h5")
+model = AutoModelForSeq2SeqLM.from_pretrained("Mbilal755/Radiology_Bart") 
+tokenizer = AutoTokenizer.from_pretrained("Mbilal755/Radiology_Bart")
 
-# Load model directly 
-import tensorflow as tf
-model = tf.keras.models.load_model(model_path)
+def summarize(input):
+    inputs = tokenizer(input, return_tensors="pt")
+    output = model.generate(inputs["input_ids"]) 
+    summary = tokenizer.decode(output[0], skip_special_tokens=True)
+    return summary
 
-# Load tokenizer
-tokenizer_path = hf_hub_download(repo_id="Mbilal755/Radiology_Bart", filename="tokenizer.json")
-with open(tokenizer_path) as f:
-    tokenizer_data = json.load(f)
-tokenizer = tokenizer_data["tokenizer"] 
-
-def summarize(text):
-  inputs = tokenizer.encode(text)
-  
-  # Run model inference
-  summary_ids = model.generate(inputs)
-  
-  summary = tokenizer.decode(summary_ids)
-  return summary
-  
 iface = gr.Interface(fn=summarize, inputs="text", outputs="text")
 
 if __name__ == "__main__":
-  iface.launch(share=True)
+    iface.launch(share=True)