test

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.8
+
+WORKDIR /workspace
+
+ADD . /workspace
+
+RUN pip install -r requirements.txt
+
+CMD [ "python" , "/workspace/app.py" ]
+
+RUN chown -R 42420:42420 /workspace
+
+ENV HOME=/workspace

README.md

@@ -1,13 +1,231 @@
----
-title: Journal
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
-short_description: Journal App
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+# AI Deploy - Tutorial - Deploy an app for sentiment analysis with Hugging Face and Flask
+
+> **Note** Access to the full documentation [here](https://docs.ovh.com/gb/en/publiccloud/ai/deploy/tuto-flask-hugging-face-sentiment-analysis/).
+
+**Last updated 3rd November, 2022.**
+
+> **Note**
+> AI Deploy is in `beta`. During the beta-testing phase, the infrastructure’s availability and data longevity are not guaranteed. Please do not use this service for applications that are in production, as this phase is not complete.
+>
+> AI Deploy is covered by **[OVHcloud Public Cloud Special Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/d2a208c-Conditions_particulieres_OVH_Stack-WE-9.0.pdf)**.
+>
+
+## Objective
+
+The purpose of this tutorial is to show you how to deploy a web service for sentiment analysis on text using Hugging Face pretrained models.<br>
+In order to do this, you will use Flask, an open-source micro framework for web development in Python. You will also learn how to build and use a custom Docker image for a Flask application.
+
+Overview of the app:
+
+![Hugging Face Overview](images/flask-hugging-face-overview.png){.thumbnail}
+
+For more information about Hugging Face, please visit <https://huggingface.co/>.
+
+## Requirements
+
+- Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.co.uk/&ovhSubsidiary=GB);
+- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/en-gb/public-cloud/) in your OVHcloud account;
+- A [user for AI Deploy](https://docs.ovh.com/gb/en/publiccloud/ai/users/);
+- [Docker](https://www.docker.com/get-started) installed on your local computer;
+- Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/);
+
+We also suggest you do some tests to find out which [Hugging Face model](https://huggingface.co/models) is right for your use case. Find examples on our [GitHub repository](https://github.com/ovh/ai-training-examples/tree/main/notebooks/natural-language-processing/text-classification/hugging-face/sentiment-analysis-twitter).
+
+## Instructions
+
+First, the tree structure of your folder should be as follows:
+
+![Flask tree structure](images/tree-flask-app.png)
+
+Find more information about the Flask application [here](https://flask.palletsprojects.com/en/2.0.x/quickstart/#a-minimal-application) to get ready to use it.
+
+### Write the Flask application
+
+Create a Python file named `app.py`.
+
+Inside that file, import your required modules:
+
+```python
+from flask import Flask, jsonify, render_template, request, make_response
+import transformers
+```
+
+Create Flask app:
+
+```python
+app = Flask(__name__)
+```
+
+Load Hugging Face models:
+
+```python
+# create a python dictionary for your models d = {<key>: <value>, <key>: <value>, ..., <key>: <value>}
+dictOfModels = {"RoBERTa" : transformers.pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english"), "BERT" : transformers.pipeline('sentiment-analysis', model="nlptown/bert-base-multilingual-uncased-sentiment")}
+# create a list of keys to use them in the select part of the html code
+listOfKeys = []
+for key in dictOfModels :
+        listOfKeys.append(key)
+```
+
+Write the inference function:
+
+```python
+def get_prediction(message,model):
+    # inference
+    results = model(message)  
+    return results
+```
+
+Define the GET method:
+
+```python
+@app.route('/', methods=['GET'])
+def get():
+    # in the select we will have each key of the list in option
+    return render_template("home.html", len = len(listOfKeys), listOfKeys = listOfKeys)
+```
+
+Define the POST method:
+
+```python
+@app.route('/', methods=['POST'])
+def predict():
+    message = request.form['message']
+    # choice of the model
+    results = get_prediction(message, dictOfModels[request.form.get("model_choice")])
+    print(f'User selected model : {request.form.get("model_choice")}')
+    my_prediction = f'The feeling of this text is {results[0]["label"]} with probability of {results[0]["score"]*100}%.'
+    return render_template('result.html', text = f'{message}', prediction = my_prediction)
+```
+
+Start your app:
+
+```python
+if __name__ == '__main__':
+    # starting app
+    app.run(debug=True,host='0.0.0.0')
+```
+
+### Write the requirements.txt file for the application
+
+The `requirements.txt` file will allow us to write all the modules needed to make our application work. This file will be useful when writing the `Dockerfile`.
+
+```console
+Flask==1.1.2
+transformers==4.4.2
+torch==1.6.0
+```
+
+Here we will mainly discuss how to write the `app.py` code, the `requirements.txt` file and the `Dockerfile`. If you want to see the whole code, please refer to the [GitHub repository](https://github.com/ovh/ai-training-examples/tree/main/apps/flask/sentiment-analysis-hugging-face-app).
+
+### Write the Dockerfile for the application
+
+Your `Dockerfile` should start with the `FROM` instruction indicating the parent image to use. In our case we choose to start from a Python image:
+
+```console
+FROM python:3.8
+```
+
+Create the home directory and add your files to it:
+
+```console
+WORKDIR /workspace
+ADD . /workspace
+```
+
+Install the `requirements.txt` file which contains your needed Python modules using a `pip install ...` command:
+
+```console
+RUN pip install -r requirements.txt
+```
+
+Define your default launching command to start the application:
+
+```console
+CMD [ "python" , "/workspace/app.py" ]
+```
+
+Give correct access rights to **ovhcloud user** (`42420:42420`):
+
+```console
+RUN chown -R 42420:42420 /workspace
+ENV HOME=/workspace
+```
+
+### Build the Docker image from the Dockerfile
+
+Launch the following command from the **Dockerfile** directory to build your application image:
+
+```console
+docker build . -t sentiment_analysis_app:latest
+```
+
+> **Note**
+> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
+>
+> The `-t` argument allows you to choose the identifier to give to your image. Usually image identifiers are composed of a **name** and a **version tag** `<name>:<version>`. For this example we chose **sentiment_analysis_app:latest**.
+>
+
+### Test it locally (optional)
+
+Launch the following **Docker command** to launch your application locally on your computer:
+
+```console
+docker run --rm -it -p 5000:5000 --user=42420:42420 sentiment_analysis_app:latest
+```
+
+> **Note**
+> The `-p 5000:5000` argument indicates that you want to execute a port redirection from the port **5000** of your local machine into the port **5000** of the Docker container. The port **5000** is the default port used by **Flask** applications.
+>
+
+> **Warning**
+> Don't forget the `--user=42420:42420` argument if you want to simulate the exact same behaviour that will occur on **AI Deploy apps**. It executes the Docker container as the specific OVHcloud user (user **42420:42420**).
+>
+
+Once started, your application should be available on `http://localhost:5000`.
+
+### Push the image into the shared registry
+
+> **Warning**
+> The shared registry of AI Deploy should only be used for testing purposes. Please consider attaching your own Docker registry. More information about this can be found [here](https://docs.ovh.com/gb/en/publiccloud/ai/training/add-private-registry/).
+>
+
+Find the adress of your shared registry by launching this command:
+
+```console
+ovhai registry list
+```
+
+Login on the shared registry with your usual OpenStack credentials:
+
+```console
+docker login -u <user> -p <password> <shared-registry-address>
+```
+
+Push the compiled image into the shared registry:
+
+```console
+docker tag sentiment_analysis_app:latest <shared-registry-address>/sentiment_analysis_app:latest
+docker push <shared-registry-address>/sentiment_analysis_app:latest
+```
+
+### Launch the AI Deploy app
+
+The following command starts a new app running your Flask application:
+
+```console
+ovhai app run --default-http-port 5000 --cpu 4 <shared-registry-address>/sentiment_analysis_app:latest
+```
+
+> **Note**
+> `--default-http-port 5000` indicates that the port to reach on the app URL is the `5000`.
+>
+> `--cpu 4` indicates that we request 4 CPUs for that app.
+>
+> Consider adding the `--unsecure-http` attribute if you want your application to be reachable without any authentication.
+>
+
+## Go further
+
+- You can also imagine deploying an Object Detection model with **Flask** in this [tutorial](https://docs.ovh.com/gb/en/publiccloud/ai/deploy/web-service-yolov5/).
+- Discover an other tool to deploy easily AI models: **Gradio**. Refer to this [documentation](https://docs.ovh.com/gb/en/publiccloud/ai/deploy/tuto-gradio-sketch-recognition/).

app.py

@@ -1,61 +1,42 @@
-from flask import Flask, request, jsonify
-from huggingface_hub import InferenceClient
+# import objects from the Flask model
+from flask import Flask, jsonify, render_template, request, make_response
+import transformers
 
-# Initialize Flask app and Hugging Face client
+# creating flask app
 app = Flask(__name__)
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-# Helper function to generate a response from the AI model
-def generate_response(message, history, system_message, max_tokens, temperature, top_p):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-    response = ""
-
-    # Streaming response from the Hugging Face model
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-
-    return response
-
-@app.route("/chat", methods=["POST"])
-def home():
-    return "Hi!"
-# API endpoint to handle requests
-@app.route("/chat", methods=["POST"])
-def chat():
-    try:
-        data = request.json
-        message = data.get("message", "")
-        history = data.get("history", [])
-        system_message = data.get("system_message", "You are a friendly chatbot.")
-        max_tokens = data.get("max_tokens", 512)
-        temperature = data.get("temperature", 0.7)
-        top_p = data.get("top_p", 0.95)
-
-        # Validate inputs
-        if not isinstance(history, list) or not all(isinstance(pair, list) for pair in history):
-            return jsonify({"error": "Invalid history format. It should be a list of [message, response] pairs."}), 400
-
-        # Generate AI response
-        response = generate_response(message, history, system_message, max_tokens, temperature, top_p)
-
-        return jsonify({"response": response})
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == "__main__":
-    app.run(debug=True)
+
+# create a python dictionary for your models d = {<key>: <value>, <key>: <value>, ..., <key>: <value>}
+dictOfModels = {"BERT" : transformers.pipeline('sentiment-analysis', model="nlptown/bert-base-multilingual-uncased-sentiment")} # feel free to add several models
+
+listOfKeys = []
+for key in dictOfModels :
+        listOfKeys.append(key) 
+
+# inference fonction
+def get_prediction(message,model):
+    # inference
+    results = model(message)  
+    return results
+
+# get method
+@app.route('/', methods=['GET'])
+def get():
+    # in the select we will have each key of the list in option
+    return render_template("home.html", len = len(listOfKeys), listOfKeys = listOfKeys)
+
+
+# post method
+@app.route('/', methods=['POST'])
+def predict():
+    message = request.form['message']
+    
+    # choice of the model
+    results = get_prediction(message, dictOfModels[request.form.get("model_choice")])
+    print(f'User selected model : {request.form.get("model_choice")}')
+    my_prediction = f'The feeling of this text is {results[0]["label"]} with probability of {results[0]["score"]*100}%.'
+ 
+    return render_template('result.html', text = f'{message}', prediction = my_prediction)
+
+if __name__ == '__main__':
+    # starting app
+    app.run(debug=True,host='0.0.0.0')

requirements.txt

@@ -1,2 +1,5 @@
-huggingface_hub==0.25.2
-flask==3.1.0
+Flask==2.1.0
+
+transformers==4.24.0
+
+torch==1.6.0

static/style.css

@@ -0,0 +1,33 @@
+html,
+body {
+  height: 100%;
+}
+
+body {
+  text-align: left;
+  margin: 0px;
+  padding: 0px;
+  background-color: #ffffff;
+}
+
+form {
+  text-align: left;
+  height: 100%;
+}
+
+.container {
+  padding: 10px;
+}
+
+.text_bottom {
+  bottom: 0;
+}
+
+#demo {
+  color: #111;
+  font-family: 'Helvetica Neue', sans-serif;
+  font-size: 22px;
+  line-height: 24px;
+  margin: 0 0 24px;
+  text-justify: inter-word;
+}

templates/home.html

@@ -0,0 +1,49 @@
+<!doctype html>
+<html lang="en">
+	<head>
+		<meta charset="utf-8">
+		<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+		<link rel="stylesheet" href="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/css/bootstrap.min.css" integrity="sha384-GJzZqFGwb1QTTN6wy59ffF1BuGJpLSa9DkKMp0DgiMDm4iYMj70gZWKYbI706tWS" crossorigin="anonymous">
+		<style>
+			.bd-placeholder-img {
+				font-size: 1.125rem;
+				text-anchor: start;
+			}
+
+			@media (min-width: 768px) {
+				.bd-placeholder-img-lg {
+					font-size: 150%;
+				}
+			}
+		</style>
+		<link rel="stylesheet" href="/static/style.css">
+		<title>Sentiment analysis on Tweets using Hugging Face</title>
+	</head>
+	<body>
+		<form method=post enctype=multipart/form-data>
+			<img src="static/flag.png" width="100%">
+			<div style="height:80px"></div>
+			<div class="container">
+				<h3 style= "color:#030d9b"><i>OVHcloud - Sentiment analysis on Tweets using Hugging Face</i></h3>
+				<hr>
+				<div style="height:50px"></div>
+				<h5>Write your text (in english): </h5>
+				<textarea name="message" rows="4" cols="50"></textarea>
+				<br/>
+				<div style= "height:40px"></div>
+				<h5>Select Hugging Face model: </h5>
+				<select name="model_choice">
+				{%for i in range(0, len)%}
+			  		<option>{{listOfKeys[i]}}</option>
+				{%endfor%}
+				</select>
+				<div style= "height:50px"></div>
+				<button class="btn btn-lg btn-primary btn-block" type="submit">Submit</button>
+				<div style= "height:100px"></div>
+				<p align="center">If you want to know more about AI Training, go <a href="https://www.ovhcloud.com/fr/public-cloud/ai-training/">here</a>.</p>
+    </form>
+		<script src="//code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
+		<script src="//cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.6/umd/popper.min.js" integrity="sha384-wHAiFfRlMFy6i5SRaxvfOCifBUQy1xHdJ/yoi7FRNXMRBu5WHdZYu1hA6ZOblgut" crossorigin="anonymous"></script>
+		<script src="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/js/bootstrap.min.js" integrity="sha384-B0UglyR+jN6CkvvICOB2joaf5I4l3gm9GU6Hc1og6Ls7i6U/mkkaduKaBhlAXv9k" crossorigin="anonymous"></script>
+	</body>
+</html>

templates/result.html

@@ -0,0 +1,43 @@
+<!doctype html>
+<html lang="en">
+	<head>
+		<meta charset="utf-8">
+		<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+		<link rel="stylesheet" href="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/css/bootstrap.min.css" integrity="sha384-GJzZqFGwb1QTTN6wy59ffF1BuGJpLSa9DkKMp0DgiMDm4iYMj70gZWKYbI706tWS" crossorigin="anonymous">
+		<style>
+			.bd-placeholder-img {
+				font-size: 1.125rem;
+				text-anchor: start;
+			}
+
+			@media (min-width: 768px) {
+				.bd-placeholder-img-lg {
+					font-size: 150%;
+				}
+			}
+		</style>
+		<link rel="stylesheet" href="/static/style.css">
+		<title>Sentiment analysis on Tweets using Hugging Face</title>
+	</head>
+	<body>
+		<form method=post enctype=multipart/form-data>
+			<img src="static/flag.png" width="100%">
+			<div style="height:80px"></div>
+			<div class="container">
+				<h3 style= "color:#030d9b"><i>OVHcloud - Sentiment analysis on Tweets using Hugging Face</i></h3>
+				<hr>
+				<div style="height:50px"></div>
+				<h4 style= "color:#030d9b">Your text:</h4>
+				<p id="demo">{{ text }}</p>
+				<div style="height:50px"></div>
+				<h4 style= "color:#030d9b">Result:</h4>
+				<p id="demo">{{ prediction }}</p>
+			</div>
+			<div style= "height:200px"></div>
+      <p align="center">If you want to know more about AI Training, go <a href="https://www.ovhcloud.com/fr/public-cloud/ai-training/">here</a>.</p>
+    </form>
+		<script src="//code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
+		<script src="//cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.6/umd/popper.min.js" integrity="sha384-wHAiFfRlMFy6i5SRaxvfOCifBUQy1xHdJ/yoi7FRNXMRBu5WHdZYu1hA6ZOblgut" crossorigin="anonymous"></script>
+		<script src="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/js/bootstrap.min.js" integrity="sha384-B0UglyR+jN6CkvvICOB2joaf5I4l3gm9GU6Hc1og6Ls7i6U/mkkaduKaBhlAXv9k" crossorigin="anonymous"></script>
+	</body>
+</html>