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import streamlit as st
from transformers import pipeline
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import torch
import numpy as np
def main():
st.title("yelp2024fall Test")
st.write("Enter a sentence for analysis:")
user_input = st.text_input("")
if user_input:
# Approach: AutoModel
model2 = AutoModelForSequenceClassification.from_pretrained("isom5240/CustomModel_yelp2024fall",
num_labels=5)
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
inputs = tokenizer(user_input,
padding=True,
truncation=True,
return_tensors='pt')
outputs = model2(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
predictions = predictions.cpu().detach().numpy()
# Get the index of the largest output value
max_index = np.argmax(predictions)
st.write(f"result (AutoModel) - Label: {max_index}")
if __name__ == "__main__":
main()
# import streamlit as st
# from transformers import pipeline
# # img2text
# def img2text(url):
# image_to_text_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
# text = image_to_text_model(url)[0]["generated_text"]
# print(text)
# return text
# # txt2Story
# def txt2story(text):
# pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
# story_txt = pipe(text)[0]['generated_text']
# print(story_txt)
# return story_txt
# # Story2Audio
# def text2audio(story_text):
# pipe = pipeline("text-to-audio", model="Matthijs/mms-tts-eng")
# audio_data = pipe(story_text)
# return audio_data
# def main():
# st.set_page_config(page_title="Your Image to Audio Story", page_icon="🦜")
# st.header("Turn Your Image to Audio Story")
# uploaded_file = st.file_uploader("Select an Image...")
# if uploaded_file is not None:
# print(uploaded_file)
# bytes_data = uploaded_file.getvalue()
# with open(uploaded_file.name, "wb") as file:
# file.write(bytes_data)
# st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
# #Stage 1: Image to Text
# st.text('Processing img2text...')
# scenario = img2text(uploaded_file.name)
# st.write(scenario)
# #Stage 2: Text to Story
# st.text('Generating a story...')
# story = txt2story(scenario)
# st.write(story)
# #Stage 3: Story to Audio data
# st.text('Generating audio data...')
# audio_data =text2audio(story)
# # Play button
# if st.button("Play Audio"):
# st.audio(audio_data['audio'],
# format="audio/wav",
# start_time=0,
# sample_rate = audio_data['sampling_rate'])
# if __name__ == "__main__":
# main() |