Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+# Define model name
+MODEL_NAME = "jojo-ai-mst/MyanmarGPT-Chat"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype="float32",  # Optimized for CPU usage
+    low_cpu_mem_usage=True  # Helps with limited memory
+)
+
+# Chatbot function
+def chatbot(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")  # Tokenize the input text
+    outputs = model.generate(
+        inputs.input_ids,
+        max_new_tokens=150,  # Limit response length
+        temperature=0.7,     # Control randomness
+        top_p=0.9            # Nucleus sampling
+    )
+    # Decode and return the generated text
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Gradio interface
+interface = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Textbox(
+        label="Chat with Burmese ChatGPT",
+        placeholder="Type your message here in Burmese...",
+        lines=5
+    ),
+    outputs=gr.Textbox(label="Response"),
+    title="Burmese ChatGPT",
+    description="A chatbot powered by MyanmarGPT-Chat for Burmese conversations."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()