Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+  "[python]": {
+    "editor.defaultFormatter": "ms-python.autopep8"
+  },
+  "python.formatting.provider": "none"
+}

README.md

@@ -1,12 +1,27 @@
 ---
-title: Product Description Generator Main
-emoji: 📈
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.41.2
+title: product-description-generator-main
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.40.1
 ---
+# Product Description Generator
+Generate SEO-compliant product descriptions using product titles and meta-keywords provided by the user.
+Implemented custom LLMChain function from the Langchain and few-shot prompting technique to generate multi-paragraph rich text product description for each product name and its corresponding keywords. Built a gradio app as a frontend demo to showcase the process.
+
+This project is hosted on HuggingFace Spaces: [Live Demo of Product Description Generator](https://huggingface.co/spaces/heliosbrahma/product-description-generator).
+
+## Steps:-
+- Provide the product name and keywords separated by commas
+- Using the custom prompt template and ChatOpenAI model, call the custom LLM chain to generate a product description
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## How to run it locally:-
+To run this app locally, first clone this repo using `git clone`.<br><br>
+Now, install all libraries by running the following command in the terminal:<br>
+```python
+pip install -r requirements.txt
+```
+<br><br>
+Now, run the app from the terminal:<br>
+```python
+gradio app.py
+```

STfile.py

@@ -0,0 +1,55 @@
+from __future__ import annotations
+import os
+from langchain.prompts import PromptTemplate
+from langchain.chat_models import ChatOpenAI
+from typing import Any
+from langchain.base_language import BaseLanguageModel
+from langchain.chains.llm import LLMChain
+import streamlit as st
+import vertexai
+from vertexai.language_models import TextGenerationModel, TextEmbeddingModel
+
+PROJECT_ID = "agileai-poc"
+
+vertexai.init(project=PROJECT_ID, location="us-central1")
+parameters = {
+    "max_output_tokens": 256,
+    "temperature": 0.2,
+    "top_p": 0.8,
+}
+
+generation_model = TextGenerationModel.from_pretrained("text-bison@001")
+print("model called")
+response = generation_model.predict(
+    "Generate a product description that is creative and SEO compliant. Emojis should be added to make product description look appealing. Begin!", **parameters)
+print(response)
+embedding_model = TextEmbeddingModel.from_pretrained("textembedding-gecko@001")
+print("model2 called")
+prompt_file = "prompt_template.txt"
+print(prompt_file)
+print(generation_model.predict("describe", **parameters))
+print(embedding_model.predict("describe", **parameters))
+tuned_model = generation_model.list_tuned_model_names()
+print("******", tuned_model)
+# prod_nm = st.text_input("Product Name")
+# keywords = st.text_input("Filters")
+# style = st.selectbox("Select the response style", [
+#                      "Funny", "Sarcastic", "Casual"])
+# generate = st.button("Generate Product Description")
+# params = {
+#     prod_nm = st.text_input("Product Name"),
+#     keywords = st.text_input("Filters"),
+#     style = st.selectbox("Select the response style", [
+#                      "Funny", "Sarcastic", "Casual"])
+#    }
+# if generate:
+#     message = st.empty()
+#     message.text("Describing...")
+#     content = (generation_model.predict(
+#         "Generate a product description that is creative and SEO compliant. Emojis should be added to make product description look appealing. Begin!", **params)
+#     )
+#     message.text("")
+#     st.write(content)
+# params={
+#     prod_num
+# }

add.py

@@ -0,0 +1,6 @@
+import langchain 
+""" // translate from python to C 
+i=int(input())
+j=int(input())
+m=i+j """
+

app.py

@@ -0,0 +1,111 @@
+from __future__ import annotations
+import os
+# import openai
+from langchain.prompts import PromptTemplate
+from langchain.chat_models import ChatOpenAI
+from typing import Any
+from langchain.base_language import BaseLanguageModel
+from langchain.chains.llm import LLMChain
+import gradio as gr
+# import streamlit as st
+
+# from google.cloud import aiplatform
+import vertexai
+from vertexai.language_models import TextGenerationModel, TextEmbeddingModel
+
+# import vertexai
+
+# PROJECT_ID = "[your-project-id]"  # @param {type:"string"}
+# vertexai.init(project=PROJECT_ID, location="us-central1")
+# from vertexai.language_models import TextGenerationModel
+
+# OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
+# OPENAI_API_KEY='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'
+
+PROJECT_ID = "agileai-poc"
+
+vertexai.init(project=PROJECT_ID, location="us-central1")
+parameters = {
+    "max_output_tokens": 256,
+    "temperature": 0.2,
+    "top_p": 0.8,
+}
+
+generation_model = TextGenerationModel.from_pretrained("text-bison@001")
+print("model called")
+response = generation_model.predict(
+    "Generate a product description that is creative and SEO compliant. Emojis should be added to make product description look appealing. Begin!", **parameters)
+print(response)
+embedding_model = TextEmbeddingModel.from_pretrained("textembedding-gecko@001")
+print("model2 called")
+prompt_file = "prompt_template.txt"
+print(generation_model.predict("describe", **parameters))
+
+
+class ProductDescGen(LLMChain):
+    """LLM Chain specifically for generating multi paragraph rich text product description using emojis."""
+
+    @classmethod
+    def from_llm(
+        cls, llm: BaseLanguageModel, prompt: str, **kwargs: Any
+    ) -> ProductDescGen:
+        """Load ProductDescGen Chain from LLM."""
+        return cls(llm=llm, prompt=prompt, **kwargs)
+
+
+def product_desc_generator(product_name, keywords):
+    with open(prompt_file, "r") as file:
+        prompt_template = file.read()
+# # llm = ChatOpenAI(
+#     #     model_name="gpt-3.5-turbo",
+#     #     temperature=0.7,
+#     #     openai_api_key=OPENAI_API_KEY,
+#     # )
+    llm = vertexai(max_output_tokens=256, temperature=0.2, top_p=0.8)
+    print("runned")
+    PROMPT = PromptTemplate(
+        input_variables=["product_name", "keywords"], template=prompt_template
+    )
+#     # llm = ChatOpenAI(
+#     #     model_name="gpt-3.5-turbo",
+#     #     temperature=0.7,
+#     #     openai_api_key=OPENAI_API_KEY,
+#     # )
+
+    # llm2 = vertexai(max_output_tokens=256, temperature=0.2, top_p=0.8)
+
+    ProductDescGen_chain = ProductDescGen.from_llm(llm=llm, prompt=PROMPT)
+    ProductDescGen_query = ProductDescGen_chain.apply_and_parse(
+        [{"product_name": product_name, "keywords": keywords}]
+    )
+#     response = generation_model.predict(
+#         "Generate a product description that is creative and SEO compliant. Emojis should be added to make product description look appealing. Begin!", **llm)
+
+    return ProductDescGen_query[0]["text"]  # , {response.text}
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1>Welcome to Product Description Generator</h1>""")
+    gr.Markdown(
+        "Generate Product Description for your products instantly!<br>"
+        "Provide product name and keywords related to that product. Click on 'Generate Description' button and multi-paragraph rich text product description will be genrated instantly.<br>"
+        "Note: Generated product description is SEO compliant and can be used to populate product information."
+    )
+
+    with gr.Tab("Generate Product Description!"):
+        product_name = gr.Textbox(
+            label="Product Name",
+            placeholder="Nike Shoes",
+        )
+        keywords = gr.Textbox(
+            label="Keywords (separated by commas)",
+            placeholder="black shoes, leather shoes for men, water resistant",
+        )
+        product_description = gr.Textbox(label="Product Description")
+        click_button = gr.Button(value="Generate Description!")
+        click_button.click(
+            product_desc_generator, [
+                product_name, keywords], product_description
+        )
+
+demo.launch(share=True)

app2.py

@@ -0,0 +1,79 @@
+from __future__ import annotations
+import os
+import vertexai
+from langchain.llms import VertexAI
+from langchain.prompts import PromptTemplate
+from langchain.chat_models import ChatOpenAI
+from typing import Any
+from langchain.base_language import BaseLanguageModel
+from langchain.chains.llm import LLMChain
+from langchain.embeddings import VertexAIEmbeddings
+import streamlit as st
+import gradio as gr
+from vertexai.language_models import TextGenerationModel
+
+vertexai.init(project="agileai-poc", location="us-central1")
+llm = VertexAI(
+    model_name="text-bison@001",
+    max_output_tokens=256,
+    temperature=0.1,
+    top_p=0.8,
+    top_k=40,
+    verbose=True,
+)
+prompt_file = "prompt_template.txt"
+
+print(prompt_file)
+
+
+class ProductDescGen(LLMChain):
+    """LLM Chain specifically for generating multi paragraph rich text product description using emojis."""
+
+    @classmethod
+    def from_llm(
+        cls, llm: BaseLanguageModel, prompt: str, **kwargs: Any
+    ) -> ProductDescGen:
+        """Load ProductDescGen Chain from LLM."""
+        return cls(llm=llm, prompt=prompt, **kwargs)
+
+
+def product_desc_generator(product_name, keywords):
+    with open(prompt_file, "r") as file:
+        prompt_template = file.read()
+
+    PROMPT = PromptTemplate(
+        input_variables=["product_name", "keywords"], template=prompt_template
+    )
+
+    ProductDescGen_chain = ProductDescGen.from_llm(llm=llm, prompt=PROMPT)
+    ProductDescGen_query = ProductDescGen_chain.apply_and_parse(
+        [{"product_name": product_name, "keywords": keywords}]
+    )
+    return ProductDescGen_query[0]["text"]
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1>Welcome to Product Description Generator</h1>""")
+    gr.Markdown(
+        "Generate Product Description for your products instantly!<br>"
+        "Provide product name and keywords related to that product. Click on 'Generate Description' button and multi-paragraph rich text product description will be genrated instantly.<br>"
+        "Note: Generated product description is SEO compliant and can be used to populate product information."
+    )
+
+    with gr.Tab("Generate Product Description!"):
+        product_name = gr.Textbox(
+            label="Product Name",
+            placeholder="Nike Shoes",
+        )
+        keywords = gr.Textbox(
+            label="Keywords (separated by commas)",
+            placeholder="black shoes, leather shoes for men, water resistant",
+        )
+        product_description = gr.Textbox(label="Product Description")
+        click_button = gr.Button(value="Generate Description!")
+        click_button.click(
+            product_desc_generator, [
+                product_name, keywords], product_description
+        )
+
+demo.launch(share=True)

appgpt.py

@@ -0,0 +1,102 @@
+# import libraries
+import vertexai
+from langchain.llms import VertexAI
+from langchain.embeddings import VertexAIEmbeddings
+
+import streamlit as st
+import requests
+from bs4 import BeautifulSoup
+
+from langchain.document_loaders import TextLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.vectorstores import DocArrayInMemorySearch
+
+vertexai.init(project="agileai-poc", location="us-central1")
+
+llm = VertexAI(
+    model_name="text-bison@001",
+    max_output_tokens=256,
+    temperature=0.1,
+    top_p=0.8,
+    top_k=40,
+    verbose=True,)
+
+embeddings = VertexAIEmbeddings()
+
+
+def get_text(url):
+    # Send a GET request to the URL
+    response = requests.get(url)
+
+    # Create a BeautifulSoup object with the HTML content
+    soup = BeautifulSoup(response.content, "html.parser")
+
+    # Find the specific element or elements containing the text you want to scrape
+    # Here, we'll find all <p> tags and extract their text
+    paragraphs = soup.find_all("p")
+
+    # Loop through the paragraphs and print their text
+    with open("text\\temp.txt", "w", encoding='utf-8') as file:
+        # Loop through the paragraphs and write their text to the file
+        for paragraph in paragraphs:
+            file.write(paragraph.get_text() + "\n")
+
+
+@st.cache_resource
+def create_langchain_index(input_text):
+    print("--indexing---")
+    get_text(input_text)
+    loader = TextLoader("text\\temp.txt", encoding='utf-8')
+    # data = loader.load()
+
+    index = VectorstoreIndexCreator(vectorstore_cls=DocArrayInMemorySearch,
+                                    embedding=embeddings).from_loaders([loader])
+    # using vertex ai embeddings initialized above
+    return index
+
+
+@st.cache_data
+def get_response(input_text, query):
+    print(f"--querying---{query}")
+    response = index.query(query, llm=llm)
+    return response
+
+
+st.title('Webpage Question and Answering')
+
+
+input_text = st.text_input("Provide the link to the webpage...")
+
+summary_response = ""
+tweet_response = ""
+ln_response = ""
+
+if input_text:
+    index = create_langchain_index(input_text)
+    summary_query = "Write a 100 words summary of the document"
+    summary_response = get_response(input_text, summary_query)
+
+    tweet_query = "Write a twitter tweet and add hashtags"
+    tweet_response = get_response(input_text, tweet_query)
+
+    ln_query = "Write a linkedin post for the document"
+    ln_response = get_response(input_text, ln_query)
+
+    with st.expander('Page Summary'):
+        st.info(summary_response)
+
+    with st.expander('Tweet'):
+        st.info(tweet_response)
+
+    with st.expander('LinkedIn Post'):
+        st.info(ln_response)
+
+st.session_state.input_text = ''
+question = st.text_input("Ask a question from the link you shared...")
+if st.button("Ask"):
+    if question:
+        index = create_langchain_index(input_text)
+        response = get_response(input_text, question)
+        st.write(response)
+    else:
+        st.warning("Please enter a question.")

google-cloud-cli-444.0.0-linux-x86_64.tar.gz

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+oid sha256:cc76b9b40508253f812af5e52d4630e90230312969eece04ccfb5328c557acac
+size 187507451

google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/anthoscli-linux-x86_64.manifest

@@ -0,0 +1,3 @@
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+platform/anthoscli_licenses/LICENSES.txt
+platform/anthoscli_licenses/lib.zip

google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/anthoscli-linux-x86_64.snapshot.json

@@ -0,0 +1,42 @@
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+        "type": "tar"
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+      "dependencies": [
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+      },
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+  "schema_version": {
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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/anthoscli.snapshot.json

@@ -0,0 +1,48 @@
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+  "components": [
+    {
+      "dependencies": [
+        "anthoscli-darwin-arm",
+        "anthoscli-darwin-x86",
+        "anthoscli-darwin-x86_64",
+        "anthoscli-linux-arm",
+        "anthoscli-linux-x86",
+        "anthoscli-linux-x86_64",
+        "anthoscli-windows-x86",
+        "anthoscli-windows-x86_64"
+      ],
+      "details": {
+        "description": "The cli for Anthos infrastructure.",
+        "display_name": "anthoscli"
+      },
+      "id": "anthoscli",
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+      "is_hidden": true,
+      "is_required": false,
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+  "schema_version": {
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+  },
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+}

google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/bq-nix.manifest

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/bq-nix.snapshot.json

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+        "type": "tar"
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+      "dependencies": [
+        "bq",
+        "core"
+      ],
+      "details": {
+        "description": "Provides the bq tool for interacting with the BigQuery service.",
+        "display_name": "BigQuery Command Line Tool (Platform Specific)"
+      },
+      "id": "bq-nix",
+      "is_configuration": false,
+      "is_hidden": true,
+      "is_required": false,
+      "platform": {
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+          "CYGWIN",
+          "LINUX",
+          "MACOSX",
+          "MSYS"
+        ]
+      },
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+      "version": {
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+        "version_string": "2.0.97"
+      }
+    }
+  ],
+  "revision": 20230822145232,
+  "schema_version": {
+    "no_update": false,
+    "url": "https://dl.google.com/dl/cloudsdk/channels/rapid/google-cloud-sdk.tar.gz",
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+  },
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+}

google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/bq.manifest

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+platform/bq/VERSION
+platform/bq/bigquery_client.py
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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/bundled-python3-unix-linux-x86_64.snapshot.json

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/core-nix.snapshot.json

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/core.snapshot.json

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/gcloud-crc32c-linux-x86_64.manifest

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+platform/gcloud-crc32c_licenses/LICENSES.txt

google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/gcloud-crc32c-linux-x86_64.snapshot.json

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/gcloud-crc32c.snapshot.json

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/gcloud-deps-linux-x86_64.snapshot.json

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/gcloud-deps.snapshot.json

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/gcloud.snapshot.json

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+}

google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/.install/gsutil-nix.manifest

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/LICENSE

@@ -0,0 +1,20 @@
+The Google Cloud CLI and its source code are licensed under Apache
+License v. 2.0 (the "License"), unless otherwise specified by an alternate
+license file.
+
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Note that if you use the Google Cloud CLI with any Google Cloud Platform
+products, your use is additionally going to be governed by the license agreement
+or terms of service, as applicable, of the underlying Google Cloud Platform
+product with which you are using the Google Cloud CLI. For example, if you are
+using the Google Cloud CLI with Google App Engine, your use would additionally
+be governed by the Google App Engine Terms of Service.
+
+This also means that if you were to create works that call Google APIs, you
+would still need to agree to the terms of service (usually, Google's
+Developer Terms of Service at https://developers.google.com/terms) for those
+APIs separately, as this code does not grant you any special rights to use
+the services.

google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/README

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+Google Cloud SDK
+Copyright 2013 Google Inc. All Rights Reserved.
+
+The Google Cloud SDK contains tools and libraries that allow you to create and
+manage resources on Google's Cloud Platform, including App Engine, Compute
+Engine, Cloud Storage, Cloud SQL, and BigQuery.
+
+For more information on how to set up and use the Cloud SDK, please see:
+  https://cloud.google.com/sdk
+
+
+System Requirements:
+  - The Cloud SDK for Python and for Go requires Python 2.7.x.
+  - The Cloud SDK for Java requires Java 1.7+ and Python 2.7.x.
+  - The SDK runs on Windows, Mac OS X, and Linux. Some of the individual tools
+    bundled with the Cloud SDK might have more stringent requirements.

google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/VERSION

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/bin/anthoscli

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+size 174851479

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google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/bin/bootstrapping/bootstrapping.py

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+# Copyright 2013 Google Inc. All Rights Reserved.
+
+"""Common bootstrapping functionality used by the wrapper scripts."""
+
+# Disables import order warning and unused import.  Setup changes the python
+# path so cloud sdk imports will actually work, so it must come first.
+# pylint: disable=C6203
+# pylint: disable=W0611
+from __future__ import absolute_import
+from __future__ import unicode_literals
+
+# Python 3 is strict about imports and we use this file in different ways, which
+# makes sub-imports difficult. In general, when a script is executed, that
+# directory is put on the PYTHONPATH. The issue is that some of the wrapper
+# scripts are executed from within the bootstrapping/ directory and some are
+# executed from within the bin/ directory.
+# pylint: disable=g-statement-before-imports
+if '.' in __name__:
+  # Here, __name__ will be bootstrapping.bootstrapping. This indicates that this
+  # file was loaded as a member of package bootstrapping. This in turn indicates
+  # that the main file that was executed was not in the bootstrapping directory,
+  # so bin/ is on the path and bootstrapping is considered a python package.
+  # Do an import of setup from this current package.
+  from . import setup  # pylint:disable=g-import-not-at-top
+else:
+  # In this case, __name__ is bootstrapping, which indicates that the main
+  # script was executed from within this directory meaning that Python doesn't
+  # consider this a package but rather the root of the PYTHONPATH. We can't do
+  # the above import because since we are not in a package, the '.' doesn't
+  # refer to anything. Just do a direct import which will find setup on the
+  # PYTHONPATH (which is just this directory).
+  import setup  # pylint:disable=g-import-not-at-top
+
+import gcloud
+import sys
+# Reorder sys.path if needed right now before more modules loaded and cached
+sys.path = gcloud.reorder_sys_path(sys.path)
+
+# pylint: disable=g-import-not-at-top
+import json
+# pylint: enable=g-import-not-at-top
+import os
+import platform
+
+from googlecloudsdk.core import config
+from googlecloudsdk.core import execution_utils
+from googlecloudsdk.core import metrics
+from googlecloudsdk.core import properties
+from googlecloudsdk.core.console import console_attr
+from googlecloudsdk.core.credentials import store as c_store
+from googlecloudsdk.core.updater import local_state
+from googlecloudsdk.core.updater import update_manager
+from googlecloudsdk.core.util import encoding
+from googlecloudsdk.core.util import files
+from googlecloudsdk.core.util import platforms
+from six.moves import input
+
+
+BOOTSTRAPPING_DIR = os.path.dirname(os.path.realpath(__file__))
+BIN_DIR = os.path.dirname(BOOTSTRAPPING_DIR)
+SDK_ROOT = os.path.dirname(BIN_DIR)
+
+
+def DisallowIncompatiblePythonVersions():
+  if not platforms.PythonVersion().IsCompatible():
+    sys.exit(1)
+
+
+def GetDecodedArgv():
+  return [console_attr.Decode(arg) for arg in sys.argv]
+
+
+def _FullPath(tool_dir, exec_name):
+  return os.path.join(SDK_ROOT, tool_dir, exec_name)
+
+
+def ExecutePythonTool(tool_dir, exec_name, *args):
+  """Execute the given python script with the given args and command line.
+
+  Args:
+    tool_dir: the directory the tool is located in
+    exec_name: additional path to the executable under the tool_dir
+    *args: args for the command
+  """
+  py_path = None  # Let execution_utils resolve the path.
+  # Gsutil allows users to set the desired Python interpreter using a separate
+  # environment variable, so as to allow users to run gsutil using Python 3
+  # without forcing the rest of the Cloud SDK to use Python 3 (as it would
+  # likely break at the time this comment was written).
+  extra_popen_kwargs = {}
+  if exec_name == 'gsutil':
+    gsutil_py = encoding.GetEncodedValue(os.environ, 'CLOUDSDK_GSUTIL_PYTHON')
+    # Since PY3, Python closes open FDs in child processes, since we need them
+    # open for completions to work we set the close_fds kwarg to Popen.
+    extra_popen_kwargs['close_fds'] = False
+    if gsutil_py:
+      py_path = gsutil_py
+
+  if exec_name == 'bq.py':
+    bq_py = encoding.GetEncodedValue(os.environ, 'CLOUDSDK_BQ_PYTHON')
+    if bq_py:
+      py_path = bq_py
+
+  if exec_name == 'dev_appserver.py':
+    if platforms.OperatingSystem.Current() != platforms.OperatingSystem.WINDOWS:
+      # Hard code the python to 'python2' which should exist on most Unix based
+      # systems.
+      py_path = 'python2'
+    else:
+      # On windows attempt to use the bundled PY2 component.
+      bundled_py2 = os.path.join(
+          SDK_ROOT, 'platform', 'bundledpython2', 'python.exe')
+      if os.path.exists(bundled_py2):
+        py_path = bundled_py2
+
+    # Set this ENV var to explicitly select the python binary to use for
+    # dev_appserver.py
+    devapp_env_py = encoding.GetEncodedValue(os.environ,
+                                             'CLOUDSDK_DEVAPPSERVER_PYTHON')
+    if devapp_env_py:
+      py_path = devapp_env_py
+
+  _ExecuteTool(
+      execution_utils.ArgsForPythonTool(
+          _FullPath(tool_dir, exec_name), *args, python=py_path),
+      **extra_popen_kwargs)
+
+
+def ExecuteJarTool(java_bin, jar_dir, jar_name, classname, flags=None, *args):
+  """Execute a given jar with the given args and command line.
+
+  Args:
+    java_bin: str, path to the system Java binary
+    jar_dir: str, the directory the jar is located in
+    jar_name: str, file name of the jar under tool_dir
+    classname: str, name of the main class in the jar
+    flags: [str], flags for the java binary
+    *args: args for the command
+  """
+  flags = flags or []
+  jar_path = _FullPath(jar_dir, jar_name)
+  classname_arg = [classname] if classname else []
+  java_args = ['-cp', jar_path] + flags + classname_arg + list(args)
+  _ExecuteTool(
+      execution_utils.ArgsForExecutableTool(java_bin, *java_args))
+
+
+def ExecuteJavaClass(java_bin,
+                     jar_dir,
+                     main_jar,
+                     main_class,
+                     java_flags=None,
+                     main_args=None):
+  """Execute a given java class within a directory of jars.
+
+  Args:
+    java_bin: str, path to the system Java binary
+    jar_dir: str, directory of jars to put on class path
+    main_jar: str, main jar (placed first on class path)
+    main_class: str, name of the main class in the jar
+    java_flags: [str], flags for the java binary
+    main_args: args for the command
+  """
+  java_flags = java_flags or []
+  main_args = main_args or []
+  jar_dir_path = os.path.join(SDK_ROOT, jar_dir, '*')
+  main_jar_path = os.path.join(SDK_ROOT, jar_dir, main_jar)
+  classpath = main_jar_path + os.pathsep + jar_dir_path
+  java_args = (['-cp', classpath]
+               + list(java_flags)
+               + [main_class]
+               + list(main_args))
+  _ExecuteTool(execution_utils.ArgsForExecutableTool(java_bin, *java_args))
+
+
+def ExecuteShellTool(tool_dir, exec_name, *args):
+  """Execute the given bash script with the given args.
+
+  Args:
+    tool_dir: the directory the tool is located in
+    exec_name: additional path to the executable under the tool_dir
+    *args: args for the command
+  """
+  _ExecuteTool(
+      execution_utils.ArgsForExecutableTool(_FullPath(tool_dir, exec_name),
+                                            *args))
+
+
+def ExecuteCMDTool(tool_dir, exec_name, *args):
+  """Execute the given batch file with the given args.
+
+  Args:
+    tool_dir: the directory the tool is located in
+    exec_name: additional path to the executable under the tool_dir
+    *args: args for the command
+  """
+  _ExecuteTool(
+      execution_utils.ArgsForCMDTool(_FullPath(tool_dir, exec_name), *args))
+
+
+def _GetToolEnv():
+  env = dict(os.environ)
+  encoding.SetEncodedValue(env, 'CLOUDSDK_WRAPPER', '1')
+  encoding.SetEncodedValue(env, 'CLOUDSDK_VERSION', config.CLOUD_SDK_VERSION)
+  encoding.SetEncodedValue(env, 'CLOUDSDK_PYTHON',
+                           execution_utils.GetPythonExecutable())
+  return env
+
+
+def _ExecuteTool(args, **extra_popen_kwargs):
+  """Executes a new tool with the given args, plus the args from the cmdline.
+
+  Args:
+    args: [str], The args of the command to execute.
+    **extra_popen_kwargs: [dict], kwargs to be unpacked in Popen call for tool.
+  """
+  execution_utils.Exec(
+      args + sys.argv[1:], env=_GetToolEnv(), **extra_popen_kwargs)
+
+
+def GetDefaultInstalledComponents():
+  """Gets the list of components to install by default.
+
+  Returns:
+    list(str), The component ids that should be installed.  It will return []
+    if there are no default components, or if there is any error in reading
+    the file with the defaults.
+  """
+  default_components_file = os.path.join(BOOTSTRAPPING_DIR,
+                                         '.default_components')
+  try:
+    with open(default_components_file) as f:
+      return json.load(f)
+  # pylint:disable=bare-except, If the file does not exist or is malformed,
+  # we don't want to expose this as an error.  Setup will just continue
+  # without installing any components by default and will tell the user how
+  # to install the components they want manually.
+  except:
+    pass
+  return []
+
+
+def WarnAndExitOnBlockedCommand(args, blocked_commands):
+  """Block certain subcommands, warn the user, and exit.
+
+  Args:
+    args: the command line arguments, including the 0th argument which is
+      the program name.
+    blocked_commands: a map of blocked commands to the messages that should be
+      printed when they're run.
+  """
+  bad_arg = None
+  for arg in args[1:]:
+    # Flags are skipped and --flag=value are skipped. It is possible for
+    # '--flag value' to result in a false positive if value happens to be a
+    # blocked command.
+    if arg and arg[0] == '-':
+      continue
+    if arg in blocked_commands:
+      bad_arg = arg
+      break
+
+  blocked = bad_arg is not None
+
+  if blocked:
+    sys.stderr.write('It looks like you are trying to run "%s %s".\n'
+                     % (args[0], bad_arg))
+    sys.stderr.write('The "%s" command is no longer needed with the '
+                     'Cloud SDK.\n' % bad_arg)
+    sys.stderr.write(blocked_commands[bad_arg] + '\n')
+    answer = input('Really run this command? (y/N) ')
+    if answer not in ['y', 'Y']:
+      sys.exit(1)
+
+
+def CheckUpdates(command_path):
+  """Check for updates and inform the user.
+
+  Args:
+    command_path: str, The '.' separated path of the command that is currently
+      being run (i.e. gcloud.foo.bar).
+  """
+  try:
+    update_manager.UpdateManager.PerformUpdateCheck(command_path=command_path)
+  # pylint:disable=broad-except, We never want this to escape, ever. Only
+  # messages printed should reach the user.
+  except Exception:
+    pass
+
+
+def CommandStart(command_name, component_id=None, version=None):
+  """Logs that the given command is being executed.
+
+  Args:
+    command_name: str, The name of the command being executed.
+    component_id: str, The component id that this command belongs to.  Used for
+      version information if version was not specified.
+    version: str, Directly use this version instead of deriving it from
+      component.
+  """
+  if version is None and component_id:
+    version = local_state.InstallationState.VersionForInstalledComponent(
+        component_id)
+  metrics.Executions(command_name, version)
+
+
+def GetActiveProjectAndAccount():
+  """Get the active project name and account for the active credentials.
+
+  For use with wrapping legacy tools that take projects and credentials on
+  the command line.
+
+  Returns:
+    (str, str), A tuple whose first element is the project, and whose second
+    element is the account.
+  """
+  project_name = properties.VALUES.core.project.Get(validate=False)
+  account = properties.VALUES.core.account.Get(validate=False)
+  return (project_name, account)
+
+
+def GetActiveImpersonateServiceAccount():
+  """Get the active impersonate_service_account property.
+
+  For use with wrapping legacy tools that take impersonate_service_account on
+  the command line.
+
+  Returns:
+    str, The name of the service account to impersonate.
+  """
+  return properties.VALUES.auth.impersonate_service_account.Get(validate=False)
+
+
+def ReadFileContents(*path_parts):
+  """Returns file content at specified relative path wrt SDK root path."""
+  return files.ReadFileContents(os.path.join(SDK_ROOT, *path_parts)).strip()
+
+
+# Register some other sources for credentials and project.
+c_store.GceCredentialProvider().Register()

google-cloud-cli-444.0.0-linux-x86_64/google-cloud-sdk/bin/bootstrapping/bq.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python
+#
+# Copyright 2013 Google Inc. All Rights Reserved.
+#
+"""A convenience wrapper for starting bq."""
+
+from __future__ import absolute_import
+from __future__ import unicode_literals
+import os
+
+import bootstrapping
+
+from googlecloudsdk.api_lib.iamcredentials import util as iamcred_util
+from googlecloudsdk.calliope import exceptions
+from googlecloudsdk.core import config
+from googlecloudsdk.core import properties
+from googlecloudsdk.core.credentials import gce
+from googlecloudsdk.core.credentials import store
+
+
+def _MaybeAddOption(args, name, value):
+  if value is None:
+    return
+  args.append('--{name}={value}'.format(name=name, value=value))
+
+
+def main():
+  """Launches bq."""
+  version = bootstrapping.ReadFileContents('platform/bq', 'VERSION')
+  bootstrapping.CommandStart('bq', version=version)
+  blocked_commands = {
+      'init': 'To authenticate, run gcloud auth.',
+  }
+  argv = bootstrapping.GetDecodedArgv()
+  bootstrapping.WarnAndExitOnBlockedCommand(argv, blocked_commands)
+
+  cmd_args = [arg for arg in argv[1:] if not arg.startswith('-')]
+  args = []
+  if cmd_args and cmd_args[0] not in ('version', 'help'):
+    # Check for credentials only if they are needed.
+    store.IMPERSONATION_TOKEN_PROVIDER = iamcred_util.ImpersonationAccessTokenProvider(
+    )
+    creds = store.Load()  # Checks if there are active credentials
+
+    project, account = bootstrapping.GetActiveProjectAndAccount()
+    adc_path = config.Paths().LegacyCredentialsAdcPath(account)
+    single_store_path = config.Paths().LegacyCredentialsBqPath(account)
+
+    if bootstrapping.GetActiveImpersonateServiceAccount():
+      args = ['--oauth_access_token', creds.token]
+    elif gce.Metadata() and account in gce.Metadata().Accounts():
+      args = ['--use_gce_service_account']
+    elif os.path.isfile(adc_path):
+      args = [
+          '--application_default_credential_file',
+          adc_path,
+          '--credential_file',
+          single_store_path,
+      ]
+    else:
+      p12_key_path = config.Paths().LegacyCredentialsP12KeyPath(account)
+      if os.path.isfile(p12_key_path):
+        args = [
+            '--service_account',
+            account,
+            '--service_account_credential_file',
+            single_store_path,
+            '--service_account_private_key_file',
+            p12_key_path,
+        ]
+      else:
+        # Don't have any credentials we can pass.
+        raise store.NoCredentialsForAccountException(account)
+
+    use_client_cert = (
+        os.getenv('GOOGLE_API_USE_CLIENT_CERTIFICATE',
+                  'false').upper() == 'TRUE')
+    if use_client_cert:
+      args.append('--mtls')
+
+    _MaybeAddOption(args, 'project_id', project)
+
+  bootstrapping.CheckUpdates('bq')
+
+  proxy_params = properties.VALUES.proxy
+  _MaybeAddOption(args, 'proxy_address', proxy_params.address.Get())
+  _MaybeAddOption(args, 'proxy_port', proxy_params.port.Get())
+  _MaybeAddOption(args, 'proxy_username', proxy_params.username.Get())
+  _MaybeAddOption(args, 'proxy_password', proxy_params.password.Get())
+  _MaybeAddOption(args, 'disable_ssl_validation',
+                  properties.VALUES.auth.disable_ssl_validation.GetBool())
+  _MaybeAddOption(args, 'ca_certificates_file',
+                  properties.VALUES.core.custom_ca_certs_file.Get())
+
+  bootstrapping.ExecutePythonTool('platform/bq', 'bq.py', *args)
+
+
+if __name__ == '__main__':
+  bootstrapping.DisallowIncompatiblePythonVersions()
+  try:
+    main()
+  except Exception as e:  # pylint: disable=broad-except
+    exceptions.HandleError(e, 'bq')