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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
from PyPDF2 import PdfReader
# Models and tokenizers setup
models = {
"Text Generator (Bloom)": {
"model": AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m"),
"tokenizer": AutoTokenizer.from_pretrained("bigscience/bloom-560m"),
},
"PDF Summarizer (T5)": {
"model": AutoModelForSeq2SeqLM.from_pretrained("t5-small"),
"tokenizer": AutoTokenizer.from_pretrained("t5-small", use_fast=False), # Use the slow tokenizer
},
"Broken Answer (T0pp)": {
"model": AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp"),
"tokenizer": AutoTokenizer.from_pretrained("bigscience/T0pp", use_fast=False), # Use the slow tokenizer
},
}
# Function for text generation
def generate_text(model_choice, input_text, max_tokens, temperature, top_p):
model_info = models[model_choice]
tokenizer = model_info["tokenizer"]
model = model_info["model"]
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model.generate(
**inputs, max_length=max_tokens, num_beams=5, early_stopping=True, temperature=temperature, top_p=top_p
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Function for PDF summarization
def summarize_pdf(pdf_file, max_tokens, temperature, top_p):
reader = PdfReader(pdf_file)
text = ""
for page in reader.pages:
text += page.extract_text()
model_info = models["PDF Summarizer (T5)"]
tokenizer = model_info["tokenizer"]
model = model_info["model"]
inputs = tokenizer("summarize: " + text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model.generate(
**inputs, max_length=max_tokens, num_beams=5, early_stopping=True, temperature=temperature, top_p=top_p
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Build Gradio interface
def launch_custom_app():
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
gr.Markdown("<h1 style='text-align: center;'>💡 Multi-Model Assistant</h1>")
gr.Markdown("<p style='text-align: center;'>Switch between text generation, PDF summarization, or quirky broken answers!</p>")
with gr.Tabs():
# Tab for Text Generation
with gr.Tab("Text Generator"):
model_choice = gr.Dropdown(choices=list(models.keys()), label="Choose a Model", value="Text Generator (Bloom)")
input_text = gr.Textbox(label="Enter Text")
max_tokens = gr.Slider(minimum=10, maximum=512, value=150, step=10, label="Max Tokens")
temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
output_text = gr.Textbox(label="Generated Text", interactive=False)
generate_button = gr.Button("Generate Text")
generate_button.click(
generate_text,
inputs=[model_choice, input_text, max_tokens, temperature, top_p],
outputs=output_text
)
# Tab for PDF Summarization
with gr.Tab("PDF Summarizer"):
pdf_file = gr.File(label="Upload a PDF File", file_types=[".pdf"])
max_tokens_pdf = gr.Slider(minimum=10, maximum=512, value=150, step=10, label="Max Tokens")
temperature_pdf = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
top_p_pdf = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
summary_output = gr.Textbox(label="PDF Summary", interactive=False)
summarize_button = gr.Button("Summarize PDF")
summarize_button.click(
summarize_pdf,
inputs=[pdf_file, max_tokens_pdf, temperature_pdf, top_p_pdf],
outputs=summary_output
)
# Tab for Broken Model
with gr.Tab("Broken Answers"):
broken_input = gr.Textbox(label="Enter Text")
broken_max_tokens = gr.Slider(minimum=10, maximum=512, value=150, step=10, label="Max Tokens")
broken_temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
broken_top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
broken_output = gr.Textbox(label="Broken Model Output", interactive=False)
broken_button = gr.Button("Generate Broken Answer")
broken_button.click(
lambda text, max_tokens, temp, top_p: generate_text("Broken Answer (T0pp)", text, max_tokens, temp, top_p),
inputs=[broken_input, broken_max_tokens, broken_temperature, broken_top_p],
outputs=broken_output
)
demo.launch()
# Launch the app
launch_custom_app()
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