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# from transformers import pipeline
# import gradio as gr
# chatbot = pipeline("text-generation", model="unsloth/DeepSeek-R1-GGUF", trust_remote_code=True)
# def chat_with_bot(user_input):
# # Generate a response from the chatbot model
# response = chatbot(user_input)
# return response[0]['generated_text']
# interface = gr.Interface(
# fn=chat_with_bot, # Function to call for processing the input
# inputs=gr.Textbox(label="Enter your message"), # User input (text)
# outputs=gr.Textbox(label="Chatbot Response", lines=10), # Model output (text)
# title="Chat with DeepSeek", # Optional: Add a title to your interface
# description="Chat with an AI model powered by DeepSeek!" # Optional: Add a description
# )
# interface.launch()
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
# Load the model and tokenizer from Hugging Face
model_name = "unsloth/Llama-3.2-3B-Instruct" # Replace with your model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Function to generate text
def generate_text(input_text, max_length=100, temperature=0.7, top_p=0.9):
# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt")
# Generate text using the model
outputs = model.generate(
inputs["input_ids"],
max_length=max_length,
temperature=temperature,
top_p=top_p,
num_return_sequences=1,
no_repeat_ngram_size=2,
)
# Decode the generated text
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_text
# Gradio Interface
def gradio_interface(input_text, max_length, temperature, top_p):
generated_text = generate_text(input_text, max_length, temperature, top_p)
return generated_text
# Create the Gradio app
app = gr.Interface(
fn=gradio_interface, # Function to call
inputs=[
gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Input Prompt"),
gr.Slider(minimum=10, maximum=500, value=100, step=10, label="Max Length"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top-p (Nucleus Sampling)"),
],
outputs=gr.Textbox(lines=10, label="Generated Text"),
title="Text Generation with Hugging Face Transformers",
description="Generate text using a Hugging Face model.",
)
# Launch the app
app.launch() |