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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import random
from datasets import load_dataset
title = "Ask Rick a Question"
description = """
The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything!
<img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>
"""
article = "Check out [the original Rick and Morty Bot](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) that this demo is based off of."
tokenizer = AutoTokenizer.from_pretrained("./gemma-2b-sciq-checkpoint")
model = AutoModelForCausalLM.from_pretrained("./gemma-2b-sciq-checkpoint")
dataset = load_dataset("allenai/sciq")
random_test_samples = dataset["test"].select(random.sample(range(0, len(dataset["test"])), 10))
examples = []
for row in random_test_samples:
examples.append([row['support'].replace('\n', ' ')])
examples.append([row['support'].replace('\n', ' '), row['correct_answer'].replace('\n', ' ')])
def predict(context, answer):
formatted = f"{context.replace('\n', ' ')}\n"
if answer is not None:
formatted = f"{context.replace('\n', ' ')}\n{answer.replace('\n', ' ')}\n"
inputs = tokenizer(formatted, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
decoded_outputs = tokenizer.decode(outputs[0], skip_special_tokens=True)
split_outputs = decoded_outputs.split("\n")
if len(split_outputs) == 6:
return {
"context": split_outputs[0],
"answer": split_outputs[1],
"question": split_outputs[2],
"distractor1": split_outputs[3],
"distractor2": split_outputs[4],
"distractor3": split_outputs[5],
}
return None
support_gr = gr.TextArea(
label="Context",
info="Make sure you use proper punctuation.",
value="Bananas are yellow and curved."
)
answer_gr = gr.TextArea(
label="Answer optional",
info="Make sure you use proper punctuation.",
value="yellow"
)
button = gr.Button("Generate", elem_id="send-btn", visible=True)
output_gr = gr.TextArea(
label="Output",
info="Make sure you use proper punctuation.",
value=""
)
gr.Interface(
fn=predict,
inputs=[support_gr, answer_gr],
outputs=[output_gr],
title=title,
description=description,
article=article,
examples=examples,
).launch() |