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Parent(s):
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Update app.py
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app.py
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# import sentencepiece
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# text = st.text_input("Enter some text")
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# if text:
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# result = sentiment_analysis(text)
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# st.json(result)
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# confidence = results[0]['score']
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# st.write(f"Sentiment: {sentiment}")
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# st.write(f"Confidence: {confidence:.2f}")
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import torch
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from transformers import AutoModelForDiffusion, DiffusionConfig
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# Specify the model parameters
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model_config = DiffusionConfig(
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image_size=64,
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num_channels=3,
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num_steps=1000,
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diffusion_steps=100,
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noise_schedule="linear",
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learning_rate=0.0001,
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)
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# Load the model
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model = AutoModelForDiffusion.from_pretrained("runwayml/stable-diffusion-v1-5", config=model_config)
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# Generate an image
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image = model.generate(text="This is a test")
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# Save the image
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torch.save(image, "image.png")
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# # Import streamlit and huggingface libraries
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# import streamlit as st
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# from transformers import pipeline
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# # Create a text to image pipeline
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# text_to_image = pipeline("text-to-image", model="runwayml/stable-diffusion-v1-5")
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# # Create a text input widget
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# text = st.text_input("Enter some text")
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# # Generate an image and display the result
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# if text:
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# result = text_to_image(text)[0]
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# st.image(result["image"])
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# import streamlit as st
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# from transformers import pipeline
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# translation = pipeline("translation_en_to_ar", model="anibahug/marian-finetuned-kde4-en-to-ar")
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# if text:
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# result = translation(text)[0]
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# st.write(f"Translated text: {result['translation_text']}")
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# text_to_speech = pipeline("Text-to-Speech")
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# if text:
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# result = text_to_speech(text)[0]
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# st.audio(result["audio"])
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# import streamlit as st
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# from transformers import pipeline, MarianTokenizer, MarianForCausalLM
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# # Set up Streamlit layout
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# st.title("Text-to-Speech App")
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# st.write("Enter text below:")
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# text_input = st.text_area("Input Text")
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# # Load the text-to-speech model
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# model_name = "facebook/wav2vec2-base-960h" # Replace with your desired TTS model
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# tokenizer = MarianTokenizer.from_pretrained(model_name)
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# model = MarianForCausalLM.from_pretrained(model_name)
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# # Generate audio
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# if st.button("Generate Audio"):
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# if text_input:
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# input_ids = tokenizer.encode(text_input, return_tensors="pt")
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# with st.spinner("Generating audio..."):
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# output = model.generate(input_ids)
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# st.audio(output[0].numpy(), format="audio/wav")
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# # Display credits and instructions
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# st.markdown("Powered by Hugging Face Spaces API and Streamlit.")
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# import streamlit as st
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# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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# # Load the text-to-speech model from Huggingface Spaces
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# model_id = "facebook/bart-base-tts"
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# model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
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# tokenizer = AutoTokenizer.from_pretrained(model_id)
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# # Create a text input field
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# text = st.text_input("Enter some text:")
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# # If the user enters some text, generate the speech
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# if text:
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# # Tokenize the text
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# inputs = tokenizer(text=text, return_tensors="pt")
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# # Generate the speech
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# generated_speech = model.generate(**inputs)
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# # Play the speech
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# st.audio(generated_speech, format="wav")
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# import sentencepiece
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import streamlit as st
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from transformers import pipeline
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sentiment_analysis = pipeline("sentiment-analysis")
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translation = pipeline("translation_en_to_ar", model="anibahug/marian-finetuned-kde4-en-to-ar")
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text = st.text_input("Enter some text")
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if text:
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result = sentiment_analysis(text)
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st.json(result)
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if text:
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result = translation(text)[0]
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st.write(f"Translated text: {result['translation_text']}")
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# confidence = results[0]['score']
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# st.write(f"Sentiment: {sentiment}")
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# st.write(f"Confidence: {confidence:.2f}")
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