Update app.py

app.py

@@ -14,25 +14,29 @@ model_name = "meta-llama/Llama-3.2-1B"
 tokenizer = AutoTokenizer.from_pretrained(model_name, token=api_token)
 model = AutoModelForCausalLM.from_pretrained(model_name, token=api_token)
 
-# Define the inference function
-def generate_text(prompt, max_length, temperature):
-    inputs = tokenizer(prompt, return_tensors="pt")
-    output = model.generate(inputs['input_ids'], max_length=max_length, temperature=temperature)
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=generate_text,
-    inputs=[
-        gr.Textbox(label="Enter your prompt", placeholder="Start typing..."),
-        gr.Slider(minimum=50, maximum=200, label="Max Length", value=100),
-        gr.Slider(minimum=0.1, maximum=1.0, label="Temperature", value=0.7),
-    ],
-    outputs="text",
-    title="LLaMA 3.2 Text Generator",
-    description="Enter a prompt to generate text using the LLaMA 3.2 model.",
-)
-
-# Launch the Gradio app
-iface.launch()
+pipe = pipeline("text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+pipe("How are you doing?")
+
+# # Define the inference function
+# def generate_text(prompt, max_length, temperature):
+#     inputs = tokenizer(prompt, return_tensors="pt")
+#     output = model.generate(inputs['input_ids'], max_length=max_length, temperature=temperature)
+#     return tokenizer.decode(output[0], skip_special_tokens=True)
+
+
+# # Create the Gradio interface
+# iface = gr.Interface(
+#     fn=generate_text,
+#     inputs=[
+#         gr.Textbox(label="Enter your prompt", placeholder="Start typing..."),
+#         gr.Slider(minimum=50, maximum=200, label="Max Length", value=100),
+#         gr.Slider(minimum=0.1, maximum=1.0, label="Temperature", value=0.7),
+#     ],
+#     outputs="text",
+#     title="LLaMA 3.2 Text Generator",
+#     description="Enter a prompt to generate text using the LLaMA 3.2 model.",
+# )
+
+# # Launch the Gradio app
+# iface.launch()