Update app.py

app.py

@@ -1,5 +1,30 @@
+# import torch
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+# import gradio as gr
+
+# # Load model and tokenizer (using CPU for broader accessibility)
+# model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float32, device_map="cpu", trust_remote_code=True)
+# tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
+
+# def generate_text(prompt):
+#     inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
+#     outputs = model.generate(**inputs, max_length=200)
+#     text = tokenizer.batch_decode(outputs)[0]
+#     return text
+
+# # Create Gradio interface
+# iface = gr.Interface(
+#     fn=generate_text,
+#     inputs=[gr.Textbox(lines=5, label="Enter your prompt")],
+#     outputs="text",
+#     title="PHI-2 Text Generator",
+#     description="Generate text using the PHI-2 generative language model",
+# )
+
+# # Launch the interface
+# iface.launch()
+
 import gradio as gr
-# Use a pipeline as a high-level helper
 from transformers import pipeline
 
 pipe = pipeline("text2text-generation", model="yeye776/t5-OndeviceAI-HomeIoT")