Update app.py

app.py

@@ -1,40 +1,27 @@
-import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
 # Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("Simbolo-Servicio/Myanmarsar-GPT")
-model = AutoModelForCausalLM.from_pretrained("Simbolo-Servicio/Myanmarsar-GPT")
+tokenizer = AutoTokenizer.from_pretrained("simbolo-ai/Myanmarsar-GPT")
+model = AutoModelForCausalLM.from_pretrained("simbolo-ai/Myanmarsar-GPT")
 
-# Use GPU if available
+# Move model to GPU if available
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-# Define generation function
-def generate_text(prompt, max_length=100, temperature=0.7):
-    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
-    output = model.generate(
-        input_ids,
-        max_length=max_length,
-        do_sample=True,
-        temperature=temperature,
-        top_k=50,
-        top_p=0.95
-    )
-    result = tokenizer.decode(output[0], skip_special_tokens=True)
-    return result
+# Input text
+input_text = "Marketing"
+input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)
 
-# Create Gradio interface
-iface = gr.Interface(
-    fn=generate_text,
-    inputs=[
-        gr.Textbox(lines=2, label="Myanmar Prompt"),
-        gr.Slider(20, 300, step=10, label="Max Length", value=100),
-        gr.Slider(0.1, 1.5, step=0.1, label="Temperature", value=0.7)
-    ],
-    outputs="text",
-    title="Myanmarsar-GPT Text Generator",
-    description="Type Burmese text and let the model generate a continuation."
+# Generate output
+output = model.generate(
+    input_ids,
+    max_length=256,
+    do_sample=True,
+    temperature=0.7,
+    top_k=50,
+    top_p=0.95
 )
 
-iface.launch()
+# Decode and print
+print(tokenizer.decode(output[0], skip_special_tokens=True))