Duplicate from breadlicker45/Muse-gen

.gitattributes

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README.md

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+---
+title: Muse text gen
+emoji: ⚡
+colorFrom: blue
+colorTo: white
+sdk: streamlit
+sdk_version: 1.9.0
+app_file: app.py
+pinned: true
+duplicated_from: breadlicker45/Muse-gen
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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+import streamlit as st
+import time
+from transformers import pipeline
+import torch
+trust_remote_code=True
+st.markdown('## Text-generation gpt Muse from Breadlicker45')
+use_auth_token=True
+@st.cache(allow_output_mutation=True, suppress_st_warning =True, show_spinner=False)
+def get_model():
+    return pipeline('text-generation', model=model, do_sample=True)
+    
+col1, col2 = st.columns([2,1])
+
+with st.sidebar:
+    st.markdown('## Model Parameters')
+
+    max_length = st.slider('Max text length', 0, 2000, 80)
+
+    num_beams = st.slider('N° tree beams search', 1, 15,  1)
+
+    early_stopping = st.selectbox(
+     'Early stopping text generation',
+     ('True', 'False'), key={'True' : True, 'False': False}, index=0)
+
+    no_ngram_repeat = st.slider('Max repetition limit', 1, 3,  1)
+    
+with col1:
+    prompt= st.text_area('Your prompt here',
+        '''2623 2619 3970 3976 2607 3973 2735 3973 2598 3985 2726 3973 2607 4009 2735 3973 2598 3973 2726 3973 2607 3973 2735 4009''') 
+        
+with col2:
+    select_model = st.radio(
+        "Select the model to use:",
+        ('MuseWeb', 'MusePy', 'MuseNeo', 'MusePy-1-1'), index = 3)
+
+    if select_model == 'MuseWeb':
+        model = 'breadlicker45/museweb'
+    elif select_model == 'MusePy':
+        model = 'breadlicker45/MusePy'
+    elif select_model == 'MuseNeo':
+        model = 'breadlicker45/MuseNeo'
+    elif select_model == 'MusePy-1-1':
+        model = 'BreadAi/MusePy-1-1'
+
+    with st.spinner('Loading Model... (This may take a while)'):
+        generator = get_model()    
+        st.success('Model loaded correctly!')
+     
+gen = st.info('Generating text...')
+answer = generator(prompt,
+                       max_length=max_length, no_repeat_ngram_size=no_ngram_repeat,
+                        early_stopping=early_stopping, num_beams=num_beams, do_sample=False)                      
+gen.empty()                      
+                       
+lst = answer[0]['generated_text']
+   
+t = st.empty()
+for i in range(len(lst)):
+    t.markdown("#### %s" % lst[0:i])
+    time.sleep(0.04)

requirements.txt

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+streamlit
+transformers
+torch