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"""from fastai.vision.all import *
import gradio as gr
learn = load_learner('tokenizer.model')
categories = ('Rasam', 'Sambar')
def classify_image(img):
pred, idx, probs = learn.predict(img)
return dict(zip(categories, map(float, probs)))
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ['sambar.jpg', 'rasam.jpg']
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch()"""
import transformers
from transformers import AutoTokenizer
import torch
import gradio as gr
from diffusers.utils.torch_utils import randn_tensor
model = "anirudh-sub/debate_model_v2.1"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
def debate_response(text):
sequences = pipeline(
text,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=500,
)
response = ""
for seq in sequences:
reponse += {seq['generated_text']}
return resposnse
intf = gr.Interface(fn=debate_response, inputs=gr.Textbox(), outputs="text")
intf.launch() |