Update app.py

app.py

@@ -1,27 +1,27 @@
-import gradio as gr, spaces
+import spaces, gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
 
-# tokenizer = AutoTokenizer.from_pretrained("LingoIITGN/ganga-1b")
-# model = AutoModelForCausalLM.from_pretrained("LingoIITGN/ganga-1b")
-
-# @spaces.GPU
-# def greet(input_text):
-#     input_token = tokenizer.encode(input_text, return_tensors="pt").to("cuda")
-#     output = model.generate(input_token, max_new_tokens=100, num_return_sequences=1, do_sample=True, top_k=50, top_p=0.95, temperature=0.7)
-#     output_text = tokenizer.batch_decode(output)[0]
-#     return output_text
-
-# demo = gr.Interface(fn=greet, inputs=["text"], outputs=["text"],)
+tokenizer = AutoTokenizer.from_pretrained("LingoIITGN/ganga-1b")
+model = AutoModelForCausalLM.from_pretrained("LingoIITGN/ganga-1b")
 
 @spaces.GPU
 def greet(input_text):
-    input_token = tokenizer.encode(input_text, return_tensors="pt").to("cpu")
-
+    input_token = tokenizer.encode(input_text, return_tensors="pt").to("cuda")
     output = model.generate(input_token, max_new_tokens=100, num_return_sequences=1, do_sample=True, top_k=50, top_p=0.95, temperature=0.7)
     output_text = tokenizer.batch_decode(output)[0]
     return output_text
 
+# demo = gr.Interface(fn=greet, inputs=["text"], outputs=["text"],)
+
+# @spaces.GPU
+# def greet(input_text):
+#     input_token = tokenizer.encode(input_text, return_tensors="pt").to("cpu")
+
+#     output = model.generate(input_token, max_new_tokens=100, num_return_sequences=1, do_sample=True, top_k=50, top_p=0.95, temperature=0.7)
+#     output_text = tokenizer.batch_decode(output)[0]
+#     return output_text
+
 
 demo = gr.Interface(fn=greet, inputs=["text"], outputs=["text"],)
 demo.launch()