🔍 Examples of Generated Samples for Code Review [DO NOT MERGE]

language_sentiment_text.py

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+# -*- coding: utf-8 -*-
+#
+# Copyright 2019 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# DO NOT EDIT! This is a generated sample ("Request",  "language_sentiment_text")
+
+# To install the latest published package dependency, execute the following:
+#   pip install google-cloud-language
+
+# sample-metadata
+#   title: Analyzing Sentiment
+#   description: Analyzing Sentiment in a String
+#   usage: python3 samples/v1/language_sentiment_text.py [--text_content "I am so happy and joyful."]
+
+# [START language_sentiment_text]
+from google.cloud import language_v1
+from google.cloud.language_v1 import enums
+
+
+def sample_analyze_sentiment(text_content):
+    """
+    Analyzing Sentiment in a String
+
+    Args:
+      text_content The text content to analyze
+    """
+
+    client = language_v1.LanguageServiceClient()
+
+    # text_content = 'I am so happy and joyful.'
+
+    # Available types: PLAIN_TEXT, HTML
+    type_ = enums.Document.Type.PLAIN_TEXT
+
+    # Optional. If not specified, the language is automatically detected.
+    # For list of supported languages:
+    # https://cloud.google.com/natural-language/docs/languages
+    language = "en"
+    document = {"content": text_content, "type": type_, "language": language}
+
+    # Available values: NONE, UTF8, UTF16, UTF32
+    encoding_type = enums.EncodingType.UTF8
+
+    response = client.analyze_sentiment(document, encoding_type=encoding_type)
+    # Get overall sentiment of the input document
+    print(u"Document sentiment score: {}".format(response.document_sentiment.score))
+    print(
+        u"Document sentiment magnitude: {}".format(
+            response.document_sentiment.magnitude
+        )
+    )
+    # Get sentiment for all sentences in the document
+    for sentence in response.sentences:
+        print(u"Sentence text: {}".format(sentence.text.content))
+        print(u"Sentence sentiment score: {}".format(sentence.sentiment.score))
+        print(u"Sentence sentiment magnitude: {}".format(sentence.sentiment.magnitude))
+
+    # Get the language of the text, which will be the same as
+    # the language specified in the request or, if not specified,
+    # the automatically-detected language.
+    print(u"Language of the text: {}".format(response.language))
+
+
+# [END language_sentiment_text]
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--text_content", type=str, default="I am so happy and joyful.")
+    args = parser.parse_args()
+
+    sample_analyze_sentiment(args.text_content)
+
+
+if __name__ == "__main__":
+    main()

speech_transcribe_async.py

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+# -*- coding: utf-8 -*-
+#
+# Copyright 2019 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# DO NOT EDIT! This is a generated sample ("LongRunningPromise",  "speech_transcribe_async")
+
+# To install the latest published package dependency, execute the following:
+#   pip install google-cloud-speech
+
+# sample-metadata
+#   title: Transcribe Audio File using Long Running Operation (Local File) (LRO)
+#   description: Transcribe a long audio file using asynchronous speech recognition
+#   usage: python3 samples/v1/speech_transcribe_async.py [--local_file_path "resources/brooklyn_bridge.raw"]
+
+# [START speech_transcribe_async]
+from google.cloud import speech_v1
+from google.cloud.speech_v1 import enums
+import io
+
+
+def sample_long_running_recognize(local_file_path):
+    """
+    Transcribe a long audio file using asynchronous speech recognition
+
+    Args:
+      local_file_path Path to local audio file, e.g. /path/audio.wav
+    """
+
+    client = speech_v1.SpeechClient()
+
+    # local_file_path = 'resources/brooklyn_bridge.raw'
+
+    # The language of the supplied audio
+    language_code = "en-US"
+
+    # Sample rate in Hertz of the audio data sent
+    sample_rate_hertz = 16000
+
+    # Encoding of audio data sent. This sample sets this explicitly.
+    # This field is optional for FLAC and WAV audio formats.
+    encoding = enums.RecognitionConfig.AudioEncoding.LINEAR16
+    config = {
+        "language_code": language_code,
+        "sample_rate_hertz": sample_rate_hertz,
+        "encoding": encoding,
+    }
+    with io.open(local_file_path, "rb") as f:
+        content = f.read()
+    audio = {"content": content}
+
+    operation = client.long_running_recognize(config, audio)
+
+    print(u"Waiting for operation to complete...")
+    response = operation.result()
+
+    for result in response.results:
+        # First alternative is the most probable result
+        alternative = result.alternatives[0]
+        print(u"Transcript: {}".format(alternative.transcript))
+
+
+# [END speech_transcribe_async]
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--local_file_path", type=str, default="resources/brooklyn_bridge.raw"
+    )
+    args = parser.parse_args()
+
+    sample_long_running_recognize(args.local_file_path)
+
+
+if __name__ == "__main__":
+    main()