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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr
import os
model_name = "unsloth/Phi-4-mini-reasoning-unsloth-bnb-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
def chat_fn(prompt: str) -> str:
if not prompt.strip():
return "Please enter a prompt."
try:
output = pipe(
prompt,
max_new_tokens=128,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
return_full_text=False,
)
generated_text = output[0]["generated_text"]
final_result = generated_text[:500].encode("utf-8", "ignore").decode("utf-8")
if not final_result.strip():
return "The model did not generate a response. Try a different prompt."
return final_result
except Exception as e:
print(f"An error occurred during text generation: {e}")
return f"[ERROR] An issue occurred while generating the response. Please try again or simplify your prompt. Details: {str(e)[:200]}"
demo_interface = gr.Interface(
fn=chat_fn,
inputs=gr.Textbox(lines=5, placeholder="Enter your prompt here...", label="Your Prompt"),
outputs=gr.Textbox(lines=10, label="Generated Response"),
title="Phi-4 Mini Reasoning Chatbot",
description="Ask the Phi-4 Mini Reasoning model anything. Responses are limited to prevent errors.",
allow_flagging="never",
)
demo_interface.launch() |