Added AI and Pages for first few examples

.gitignore

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+__pycache__
+cache

AI/__init__.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10

AI/question_answering.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import torch
+import torch_directml
+from transformers import pipeline
+
+def getDevice(DEVICE):
+    device = None
+    if DEVICE == "cpu":
+        device = torch.device("cpu")
+        dtype = torch.float32
+    elif DEVICE == "cuda":
+        device = torch.device("cuda")
+        dtype = torch.float16
+    elif DEVICE == "directml":
+        device = torch_directml.device()
+        dtype = torch.float16
+    return device
+
+def loadGenerator(device):
+    generator = pipeline("question-answering") # .to(device)
+    return generator
+
+def query(generator, question, context):
+    output = generator(
+        question=question,
+        context=context,
+    )
+    return output
+
+def clearCache(DEVICE, generator):
+    generator.tokenizer.save_pretrained("cache")
+    generator.model.save_pretrained("cache")
+    del generator
+    if DEVICE == "directml":
+        torch_directml.empty_cache()

AI/sentiment_analysis.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import torch
+import torch_directml
+from transformers import pipeline
+
+def getDevice(DEVICE):
+    device = None
+    if DEVICE == "cpu":
+        device = torch.device("cpu")
+        dtype = torch.float32
+    elif DEVICE == "cuda":
+        device = torch.device("cuda")
+        dtype = torch.float16
+    elif DEVICE == "directml":
+        device = torch_directml.device()
+        dtype = torch.float16
+    return device
+
+def loadClassifier(device):
+    classifier = pipeline("sentiment-analysis") # .to(device)
+    return classifier
+
+def classify(classifier, text):
+    output = classifier(text)
+    return output
+
+def clearCache(DEVICE, classifier):
+    classifier.tokenizer.save_pretrained("cache")
+    classifier.model.save_pretrained("cache")
+    del classifier
+    if DEVICE == "directml":
+        torch_directml.empty_cache()

AI/summarization.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import torch
+import torch_directml
+from transformers import pipeline
+
+def getDevice(DEVICE):
+    device = None
+    if DEVICE == "cpu":
+        device = torch.device("cpu")
+        dtype = torch.float32
+    elif DEVICE == "cuda":
+        device = torch.device("cuda")
+        dtype = torch.float16
+    elif DEVICE == "directml":
+        device = torch_directml.device()
+        dtype = torch.float16
+    return device
+
+def loadSummarizer(device):
+    summarizer = pipeline("summarization") # .to(device)
+    return summarizer
+
+def summarize(summarizer, text):
+    output = summarizer(text)
+    return output
+
+def clearCache(DEVICE, summarizer):
+    summarizer.tokenizer.save_pretrained("cache")
+    summarizer.model.save_pretrained("cache")
+    del summarizer
+    if DEVICE == "directml":
+        torch_directml.empty_cache()

AI/text_generation.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import torch
+import torch_directml
+from transformers import pipeline
+
+def getDevice(DEVICE):
+    device = None
+    if DEVICE == "cpu":
+        device = torch.device("cpu")
+        dtype = torch.float32
+    elif DEVICE == "cuda":
+        device = torch.device("cuda")
+        dtype = torch.float16
+    elif DEVICE == "directml":
+        device = torch_directml.device()
+        dtype = torch.float16
+    return device
+
+def loadGenerator(device):
+    generator = pipeline("text-generation") # .to(device)
+    return generator
+
+def generate(generator, text):
+    output = generator(text)
+    return output
+
+def clearCache(DEVICE, generator):
+    generator.tokenizer.save_pretrained("cache")
+    generator.model.save_pretrained("cache")
+    del generator
+    if DEVICE == "directml":
+        torch_directml.empty_cache()

AI/zero_shot_classification.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import torch
+import torch_directml
+from transformers import pipeline
+
+def getDevice(DEVICE):
+    device = None
+    if DEVICE == "cpu":
+        device = torch.device("cpu")
+        dtype = torch.float32
+    elif DEVICE == "cuda":
+        device = torch.device("cuda")
+        dtype = torch.float16
+    elif DEVICE == "directml":
+        device = torch_directml.device()
+        dtype = torch.float16
+    return device
+
+def loadGenerator(device):
+    generator = pipeline("zero-shot-classification") # .to(device)
+    return generator
+
+def classify(generator, text, labels=["education", "politics", "business"]):
+    output = generator(text, candidate_labels=labels)
+    return output
+
+def clearCache(DEVICE, generator):
+    generator.tokenizer.save_pretrained("cache")
+    generator.model.save_pretrained("cache")
+    del generator
+    if DEVICE == "directml":
+        torch_directml.empty_cache()

Home.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+def main():
+    st.title('Home Page')
+    dataframe = pd.DataFrame(
+        np.random.randn(10, 20),
+        columns=('col %d' % i for i in range(20))
+    )
+    st.dataframe(dataframe.style.highlight_max(axis=0))
+    
+
+if __name__ == '__main__':
+    main()

README.md

@@ -5,8 +5,11 @@ colorFrom: blue
 colorTo: purple
 sdk: streamlit
 sdk_version: 1.29.0
-app_file: app.py
+app_file: Home.py
 pinned: false
 ---
+BasicTransformersExample
+
+Author: Ricardo Lisboa Santos
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

pages/Question_Answering.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import streamlit as st
+import AI.question_answering as ai
+
+def run():
+
+    st.set_page_config(page_title="Question Answering", page_icon="📈")
+
+    st.markdown("# Question Answering")
+    st.write('Give some context. Ask some questions.')
+    context = st.text_area('Enter your context here.')
+    question = st.text_input('Enter your question here.')
+    if st.button('Click me to run'):
+        progress_bar = st.sidebar.progress(0)
+        status_text = st.sidebar.empty()
+        with st.spinner(text='Loading Model'):
+            status_text.text("Getting Device")
+            device = ai.getDevice("cpu")
+            progress_bar.progress(30)
+            status_text.text("Loading Model")
+            model = ai.loadGenerator(device)
+            progress_bar.progress(60)
+            status_text.text("Generating Answer")
+            output = ai.query(model, question, context)
+            progress_bar.progress(90)
+            status_text.text("Clearing Cache")
+            ai.clearCache("cpu", model)
+            progress_bar.progress(100)
+            status_text.text("Done")
+            st.code(output.get('answer'))
+            # st.success('Done')
+
+if __name__ == '__main__':
+    run()

pages/Sentiment_Analysis.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import streamlit as st
+import time
+import AI.sentiment_analysis as ai
+
+
+
+def run():
+
+    st.set_page_config(page_title="Sentiment Analysis", page_icon="📈")
+    # setSidebar()
+    # st.write('Sentiment Analysis')
+    st.markdown("# Sentiment Analysis")
+    input = st.text_input('Enter your prompt here.')
+    if st.button('Click me to run'):
+        progress_bar = st.sidebar.progress(0)
+        status_text = st.sidebar.empty()
+        loading_text='Loading Model'
+        with st.spinner(text=loading_text):
+            status_text.text("Getting Device")
+            device = ai.getDevice("cpu")
+            progress_bar.progress(30)
+            status_text.text("Loading Model")
+            model = ai.loadClassifier(device)
+            progress_bar.progress(60)
+            status_text.text("Classifying")
+            output = ai.classify(model, input)
+            progress_bar.progress(90)
+            ai.clearCache("cpu", model)
+            progress_bar.progress(100)
+            status_text.text("Done")
+            if output[0].get('label') == 'NEGATIVE':
+                st.error(' 😔 ' + output[0].get('label'))
+            elif output[0].get('label') == 'POSITIVE':
+                st.success(' 😃 ' + output[0].get('label'))
+
+# def setSidebar():
+#     st.sidebar.header("Sentiment Analysis")
+#     st.sidebar.write(
+#         """This demo illustrates a combination of plotting and animation with
+#     Streamlit. We're generating a bunch of random numbers in a loop for around
+#     5 seconds. Enjoy!"""
+#     )
+
+if __name__ == '__main__':
+    run()

pages/Summarization.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import streamlit as st
+import time
+import AI.summarization as ai
+
+
+
+def run():
+
+    st.set_page_config(page_title="Summarization", page_icon="📈")
+    # setSidebar()
+    # st.write('Summarization')
+    st.markdown("# Summarization")
+    input = st.text_area('Enter your LOOOONG text here.')
+    if st.button('Click me to run'):
+        progress_bar = st.sidebar.progress(0)
+        status_text = st.sidebar.empty()
+        loading_text='Loading Model'
+        with st.spinner(text=loading_text):
+            status_text.text("Getting Device")
+            device = ai.getDevice("cpu")
+            progress_bar.progress(30)
+            status_text.text("Loading Model")
+            model = ai.loadSummarizer(device)
+            progress_bar.progress(60)
+            status_text.text("Summarizing")
+            output = ai.summarize(model, input)
+            progress_bar.progress(90)
+            ai.clearCache("cpu", model)
+            progress_bar.progress(100)
+            status_text.text("Done")
+            st.code(output[0].get('summary_text'))
+
+if __name__ == '__main__':
+    run()

pages/Text_Generation.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import streamlit as st
+import AI.text_generation as ai
+
+def run():
+
+    st.set_page_config(page_title="Text Generation", page_icon="📈")
+
+    st.markdown("# Text Generation")
+    st.write('Generate some text.')
+    input = st.text_input('Enter your prompt here.')
+    if st.button('Click me to run'):
+        progress_bar = st.sidebar.progress(0)
+        status_text = st.sidebar.empty()
+        with st.spinner(text='Loading Model'):
+            status_text.text("Getting Device")
+            device = ai.getDevice("cpu")
+            progress_bar.progress(30)
+            status_text.text("Loading Model")
+            model = ai.loadGenerator(device)
+            progress_bar.progress(60)
+            status_text.text("Generating Answer")
+            output = ai.generate(model, input)
+            progress_bar.progress(90)
+            status_text.text("Clearing Cache")
+            ai.clearCache("cpu", model)
+            progress_bar.progress(100)
+            status_text.text("Done")
+            st.code(output[0].get('generated_text'))
+            # st.success('Done')
+
+if __name__ == '__main__':
+    run()

pages/Zero_Shot_Classification.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10
+
+import streamlit as st
+import AI.zero_shot_classification as ai
+
+def run():
+
+    st.set_page_config(page_title="Zero Shot Classification", page_icon="📈")
+
+    st.markdown("# Zero Shot Classification")
+    st.write('Add some labels and then add a text to classify.')
+    labels_box = st.text_area('Enter your labels here.')
+    st.markdown('<p class="breadcrumb">{}</p>'.format(labels_box))
+    text = st.text_input('Enter your text here.')
+    if st.button('Click me to run'):
+        progress_bar = st.sidebar.progress(0)
+        status_text = st.sidebar.empty()
+        with st.spinner(text='Loading Model'):
+            status_text.text("Getting Device")
+            device = ai.getDevice("cpu")
+            progress_bar.progress(30)
+            status_text.text("Loading Model")
+            model = ai.loadGenerator(device)
+            progress_bar.progress(60)
+            status_text.text("Generating Answer")
+            output = ai.classify(model, question, context)
+            progress_bar.progress(90)
+            status_text.text("Clearing Cache")
+            ai.clearCache("cpu", model)
+            progress_bar.progress(100)
+            status_text.text("Done")
+            st.code(output.get('answer'))
+            # st.success('Done')
+
+if __name__ == '__main__':
+    run()

pages/__init__.py

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+# Author: Ricardo Lisboa Santos
+# Creation date: 2024-01-10