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from huggingface_hub import login
import os
huggingfacetoken = os.getenv('hf_write')
login(token = huggingfacetoken)
import gradio as gr
from transformers import pipeline
#pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-3B-Instruct")
#pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-3B") gave some preamble
#pipe = pipeline("text-generation", model="mistralai/Mixtral-8x7B-Instruct-v0.1") exceeded free memory
#pipe = pipeline("text-generation", model="mistralai/Mistral-7B-v0.1") exceeded free memory
#pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B")
pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct")
#pipe = pipeline("text-generation", model="openai-community/gpt2")
#pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
def response(age, story):
#messages = f"this is the 300 word story of {story} in words a {age} year old can understand."
messages = f"You are a school teacher telling stories to children. Retell the story of {story} in 300 words using language a {age} year old can understand."
sequences = pipe(messages, max_length=500)
gen_text = sequences[0]["generated_text"][len(messages):]
return gen_text
demo = gr.Interface(
response,
inputs=[gr.Slider(minimum=5, maximum=10, step=1, value=5, label="Enter your age: ", info="Select an age between 5 and 10."),
gr.Dropdown(
["Cinderella", "Snow White", "Little Red Riding Hood"], label="Select the Story: ", info="Select the story you want to read."),
],
outputs=[gr.Textbox(label="Here's the story: ", lines=3)],
title="Tell me a story!",
)
demo.launch()
# import gradio as gr
# from transformers import pipeline
# pipe = pipeline("text-generation", model="openai-community/gpt2")
# def response(messages):
# return pipe(messages)
# demo = gr.Interface(
# response,
# inputs=[gr.Textbox(label="Input your question: ", lines=3)],
# outputs=[gr.Textbox(label="Here's the answer: ", lines=3)],
# title="Ask me anything!",
# )
# demo.launch()
# import gradio as gr
# from transformers import pipeline
# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
# def predict(image):
# predictions = pipeline(image)
# return {p["label"]: p["score"] for p in predictions}
# demo = gr.Interface(
# predict,
# inputs=gr.Image(label="Upload hot dog candidate", type="filepath"),
# outputs=gr.Label(num_top_classes=2),
# title="Hot Dog? Or Not?",
# )
# demo.launch() |