Update app.py

app.py

@@ -1,20 +1,28 @@
 import gradio as gr
-import torch
+import json
 from huggingface_hub import hf_hub_download
 
-model_path = hf_hub_download(repo_id="Mbilal755/Radiology_Bart", filename="pytorch_model.bin")
-model = torch.load(model_path)
+model_path = hf_hub_download(repo_id="Mbilal755/Radiology_Bart", filename="tf_model.h5")
+
+# Load model directly 
+import tensorflow as tf
+model = tf.keras.models.load_model(model_path)
 
 # Load tokenizer
-tokenizer = hf_hub_download(repo_id="Mbilal755/Radiology_Bart", filename="tokenizer.json") 
-tokenizer = tokenizer.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(text, return_tensors="pt") 
-  summary_ids = model.generate(inputs["input_ids"])  
-  summary = tokenizer.decode(summary_ids[0])
+  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__":