updated logic with more examples

app.py

@@ -1,11 +1,389 @@
 import streamlit as st
+import time
+from transformers import pipeline
+from datasets import load_dataset, Audio
 
-st.title('🤗 Transformers Library examples')
-st.divider() 
-st.write("Sentiment Analysis")
-st.write("'Zero-shot classification")
-st.write("Text Generation")
-st.write("'Mask Filling")
-st.write("Named Entity Recognition")
-st.write("Question Answering")
-st.write("Translation")
+st.set_page_config(page_title="🤗 Transformers Library examples",layout="wide")
+
+st.title('🤗 :rainbow[Transformers Library examples]')
+
+# Done
+# function for Sentiment Analysis or Text classification model
+def sentiment_analysis():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+        classifier = pipeline("sentiment-analysis")
+        results = classifier("Transformers library is very helpful.")
+
+        
+        st.write("Output:")
+        st.success(results)
+        st.divider()
+
+        st.subheader("Example: Multiple statements analysis")
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier([
+                "This is quick tutorial site.",
+                "I learnt new topics today.",
+                "I do not like lengthy tutorials."
+            ])
+        '''
+        st.code(code, language='python')
+
+        results = classifier([
+            "This is quick tutorial site.",
+            "I learnt new topics today.",
+            "I do not like lengthy tutorials."
+        ])
+
+        st.write("Output:")
+        st.success(results)
+
+
+# function for Sentiment Analysis or Text classification model
+def text_generation():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        classifier = pipeline("sentiment-analysis")
+
+
+
+# function for Sentiment Analysis or Text classification model
+def summarization():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        classifier = pipeline("sentiment-analysis")
+     
+# DONE
+# function for Image Classification model
+def image_classification():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        st.image("./data/dog.jpeg", width=250, use_column_width=100)
+        with st.spinner('Wait for it...'):
+            time.sleep(8)
+        vision_classifier = pipeline(model="google/vit-base-patch16-224")
+        preds = vision_classifier(images="./data/dog.jpeg")
+        st.success("Output:")
+        st.json(preds)
+
+
+# function for Sentiment Analysis or Text classification model
+def image_segmentation():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        classifier = pipeline("sentiment-analysis")
+
+
+# function for Sentiment Analysis or Text classification model
+def object_detection():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        classifier = pipeline("sentiment-analysis")
+
+
+# function for Audio Classification model
+def audio_classification():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        classifier = pipeline("sentiment-analysis")
+
+
+# function forAutomatic Speech Recognition model
+def automatic_speech_recognition():
+    minds = load_dataset("PolyAI/minds14", name="en-US", split="train")
+    minds = minds.train_test_split(test_size=0.2)
+    st.write(minds)
+    minds = minds.remove_columns(["path", "transcription", "english_transcription", "lang_id"])
+    st.write("minds[train][0] " , minds["train"][0])
+    labels = minds["train"].features["intent_class"].names
+    st.write("labels " ,labels)
+    label2id, id2label = dict(), dict()
+    for i, label in enumerate(labels):
+        label2id[label] = str(i)
+        id2label[str(i)] = label
+    st.write("label2id - id2label" , label2id , id2label)
+
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("automatic-speech-recognition")
+            results = transcriber("./data/mlk.flac")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+        transcriber = pipeline(task="automatic-speech-recognition")
+        results = transcriber("./data/audio.m4a")
+        st.write("Output:")
+        st.success(results)
+
+
+# function for Image Captioningn model
+def image_captioning():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        classifier = pipeline("sentiment-analysis")
+
+
+# function for Mask Filling model
+def mask_filling():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        classifier = pipeline("sentiment-analysis")        
+
+
+
+
+# function for Document Question Answering model
+def document_question_answering():
+        code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+        st.code(code, language='python')
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        
+
+
+
+# function for Named Entity Recognition model
+def named_entity_recognition():
+    code = '''
+            from transformers import pipeline
+
+            classifier = pipeline("sentiment-analysis")
+            results = classifier("Transformers library is very helpful.")
+        '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+
+        classifier = pipeline("sentiment-analysis")  
+
+
+# function for translation model
+def translation():
+    code = '''
+        from transformers import pipeline
+
+        classifier = pipeline("sentiment-analysis")
+        results = classifier("Transformers library is very helpful.")
+    '''
+    st.code(code, language='python')
+    if st.button("Run Test ", type="primary"):      
+        with st.spinner('Wait for it...'):
+            time.sleep(5)
+        classifier = pipeline("sentiment-analysis")
+
+
+
+
+col1, col2 = st.columns(2)
+'''
+   - `"audio-classification"`: will return a [`AudioClassificationPipeline`].
+            - `"automatic-speech-recognition"`: will return a [`AutomaticSpeechRecognitionPipeline`].
+            - `"conversational"`: will return a [`ConversationalPipeline`].
+            - `"depth-estimation"`: will return a [`DepthEstimationPipeline`].
+            - `"document-question-answering"`: will return a [`DocumentQuestionAnsweringPipeline`].
+            - `"feature-extraction"`: will return a [`FeatureExtractionPipeline`].
+            - `"fill-mask"`: will return a [`FillMaskPipeline`]:.
+            - `"image-classification"`: will return a [`ImageClassificationPipeline`].
+            - `"image-feature-extraction"`: will return an [`ImageFeatureExtractionPipeline`].
+            - `"image-segmentation"`: will return a [`ImageSegmentationPipeline`].
+            - `"image-to-image"`: will return a [`ImageToImagePipeline`].
+            - `"image-to-text"`: will return a [`ImageToTextPipeline`].
+            - `"mask-generation"`: will return a [`MaskGenerationPipeline`].
+            - `"object-detection"`: will return a [`ObjectDetectionPipeline`].
+            - `"question-answering"`: will return a [`QuestionAnsweringPipeline`].
+            - `"summarization"`: will return a [`SummarizationPipeline`].
+            - `"table-question-answering"`: will return a [`TableQuestionAnsweringPipeline`].
+            - `"text2text-generation"`: will return a [`Text2TextGenerationPipeline`].
+            - `"text-classification"` (alias `"sentiment-analysis"` available): will return a
+              [`TextClassificationPipeline`].
+            - `"text-generation"`: will return a [`TextGenerationPipeline`]:.
+            - `"text-to-audio"` (alias `"text-to-speech"` available): will return a [`TextToAudioPipeline`]:.
+            - `"token-classification"` (alias `"ner"` available): will return a [`TokenClassificationPipeline`].
+            - `"translation"`: will return a [`TranslationPipeline`].
+            - `"translation_xx_to_yy"`: will return a [`TranslationPipeline`].
+            - `"video-classification"`: will return a [`VideoClassificationPipeline`].
+            - `"visual-question-answering"`: will return a [`VisualQuestionAnsweringPipeline`].
+            - `"zero-shot-classification"`: will return a [`ZeroShotClassificationPipeline`].
+            - `"zero-shot-image-classification"`: will return a [`ZeroShotImageClassificationPipeline`].
+            - `"zero-shot-audio-classification"`: will return a [`ZeroShotAudioClassificationPipeline`].
+            - `"zero-shot-object-detection"`: will return a [`ZeroShotObjectDetectionPipeline`].
+
+'''
+
+
+with col1:
+    taskType = st.radio(
+        "Select a type of task to perform",
+        [
+            "Sentiment Analysis or Text classification", 
+            "Text Generation",
+            "Summarization",
+            "Image Classification",
+            "Image Segmentation",
+            "Object Detection",
+            "Audio Classification",
+            "Automatic Speech Recognition",
+            "Visual Question Answering",
+            "Document Question Answering",
+            "Image Captioning",
+
+            "Mask Filling",
+            "Named Entity Recognition", 
+            "Translation"
+        ],
+        captions = [
+            "**pipeline(task=“sentiment-analysis”)**",
+            "pipeline(task=“text-generation”)",
+            "pipeline(task=“summarization”)",
+            "pipeline(task=“image-classification”)",
+            "pipeline(task=“image-segmentation”)",
+            "pipeline(task=“object-detection”)",
+            "pipeline(task=“audio-classification”)",
+            "pipeline(task=“automatic-speech-recognition”)",
+            "pipeline(task=“vqa”)",
+            "pipeline(task=“document-question-answering”)",
+            "pipeline(task=“image-to-text”)"
+            
+            "Mask Filling",
+            "Named Entity Recognition",
+            "Translation"
+         ], index=0)
+
+
+with col2:
+     
+    st.subheader(f"Example: {taskType}")
+    if taskType == "Sentiment Analysis or Text classification":
+        sentiment_analysis()
+
+    if taskType == "Text Generation":
+        text_generation()
+
+    if taskType == "Summarization":
+        summarization()
+
+    if taskType == "Image Classification":
+        image_classification()
+
+    if taskType == "Image Segmentation":
+        image_segmentation()
+
+    if taskType == "Object Detection":
+        object_detection()
+
+    if taskType == "Audio Classification":
+        audio_classification()
+
+    if taskType == "Automatic Speech Recognition":
+       automatic_speech_recognition()
+
+    if taskType == "Document Question Answering":
+         document_question_answering()
+
+    if taskType == "Image Captioning":
+         image_captioning()
+
+    if taskType == "Mask Filling":
+        mask_filling()
+
+    if taskType == "Named Entity Recognition":
+        named_entity_recognition()
+
+    if taskType == "Translation":
+        translation()
+
+