Create app.py

app.py

@@ -0,0 +1,23 @@
+import torch 
+from transformers import AutoTokenizer, AutoModelForCausalLM
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+tokenizer = AutoTokenizer.from_pretrained("alibidaran/medical_transcription_generator")
+
+model = AutoModelForCausalLM.from_pretrained("alibidaran/medical_transcription_generator").to(device)
+def generate_text(Text,Max_length,Temperature):
+  torch.manual_seed(32)
+  tokenizer.pad_token_id=tokenizer.eos_token_id
+  with torch.no_grad():
+    input_ids = tokenizer(Text, return_tensors="pt")["input_ids"].to('cpu')
+    output=model.generate(input_ids,max_new_tokens=Max_length,do_sample=True, temperature=Temperature, top_p=0.90,top_k=10)
+    return tokenizer.decode(output[0])
+demo=gr.Interface(
+    generate_text,
+    ['text',
+     gr.Slider(50,2000,value=100,step=10),
+     gr.Slider(0,2,value=0.7,step=0.1)],
+    'text',
+      theme=gr.themes.Base(primary_hue='blue',secondary_hue='cyan'),
+      description="Medical Trasncript Generator"
+)
+demo.launch()