Create a new directory for your project and navigate into it:\n$ mkdir sentiment-analysis-app\n$ cd sentiment-analysis-app\n<\/code><\/pre>\n<\/li>\nInitialize a new virtual environment using venv<\/code>:\n$ python3 -m venv env\n<\/code><\/pre>\n<\/li>\nActivate the virtual environment:\n\n- For macOS and Linux:
\nshell
\n$ source env\/bin\/activate<\/code><\/li>\n- For Windows:
\nshell
\n$ .envScriptsactivate<\/code><\/li>\n<\/ul>\n<\/li>\n- Install the required Python packages:\n
$ pip install flask openai\n<\/code><\/pre>\n<\/li>\n- Create a new Python file named
app.py<\/code> in your project directory.<\/p>\n<\/li>\n<\/ol>\nNow that we have our project environment set up, let’s proceed with building our sentiment analysis application.<\/p>\n
Creating the Flask Web Application<\/h2>\n
Open app.py<\/code> in your favorite text editor and add the following code:<\/p>\nfrom flask import Flask, render_template, request\nimport openai\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = \"mysecretkey\"\n\nopenai.api_key = \"<your_openai_api_key>\"\n<\/code><\/pre>\nIn the code above, we import the required Flask packages, create a Flask application, and set the SECRET_KEY<\/code>. Make sure to replace <your_openai_api_key><\/code> with your actual OpenAI API key.<\/p>\nNext, we will define the routes that our web application will handle. Add the following code to app.py<\/code>:<\/p>\n@app.route(\"\/\", methods=[\"GET\", \"POST\"])\ndef home():\n if request.method == \"POST\":\n text = request.form.get(\"text\")\n sentiment = analyze_sentiment(text)\n return render_template(\"index.html\", sentiment=sentiment)\n return render_template(\"index.html\")\n<\/code><\/pre>\nIn the code snippet above, we define a route for the root URL, \/<\/code>. Inside the route, we check if the request method is POST<\/code>. If so, we retrieve the user-provided text from the form submission and pass it to the analyze_sentiment()<\/code> function. Finally, we render the index.html<\/code> template with the sentiment result.<\/p>\nPerforming Sentiment Analysis with OpenAI GPT-3<\/h2>\n
Now, let’s define the analyze_sentiment()<\/code> function that will perform sentiment analysis using OpenAI GPT-3. Add the following code to app.py<\/code>:<\/p>\ndef analyze_sentiment(text):\n prompt = text + \"nnSentiment:\"\n\n response = openai.Completion.create(\n engine=\"text-davinci-003\",\n prompt=prompt,\n max_tokens=1,\n temperature=0.0,\n n = 1,\n stop = None,\n )\n\n sentiment = response.choices[0].text.strip()\n\n return sentiment\n<\/code><\/pre>\nIn the code above, we define the analyze_sentiment()<\/code> function that takes the text<\/code> parameter as input. We create a prompt by concatenating the user-provided text with the Sentiment:<\/code> suffix. We then use the OpenAI GPT-3 text-davinci-003<\/code> engine to generate a response.<\/p>\nWe set the max_tokens<\/code> argument to 1<\/code> to limit the response to a single token. The temperature<\/code> argument controls the randomness of the response, and a value of 0.0<\/code> ensures deterministic behavior. The n<\/code> argument is set to 1<\/code> to request a single response from the GPT-3 model. Finally, the stop<\/code> argument is set to None<\/code> to allow the model to generate a complete sentiment.<\/p>\nWe extract the sentiment from the response and return it from the function.<\/p>\n
Creating the HTML Templates<\/h2>\n
Next, let’s create the HTML templates needed for our web application.<\/p>\n
Create a new directory called templates<\/code> in your project directory. Inside the templates<\/code> directory, create a new file called index.html<\/code> with the following content:<\/p>\n<!DOCTYPE html>\n<html>\n <head>\n <title>Sentiment Analysis App<\/title>\n <\/head>\n <body>\n <h1>Sentiment Analysis App<\/h1>\n <form action=\"\/\" method=\"post\">\n <label for=\"text\">Enter text:<\/label>\n <textarea id=\"text\" name=\"text\" rows=\"4\" cols=\"50\"><\/textarea>\n <button type=\"submit\">Analyze Sentiment<\/button>\n <\/form>\n {% if sentiment %}\n <h2>Sentiment: {{ sentiment }}<\/h2>\n {% endif %}\n <\/body>\n<\/html>\n<\/code><\/pre>\nIn the code above, we define a simple HTML form that allows users to enter text. The form is submitted to the root URL \/<\/code> using the POST<\/code> method. After submitting the form, the sentiment result is displayed if available.<\/p>\nRunning the Application<\/h2>\n
Before running the application, make sure you have activated the virtual environment. If not, run the following command in your project directory:<\/p>\n
\n- For macOS and Linux:
\nshell
\n$ source env\/bin\/activate<\/code><\/li>\n- For Windows:
\nshell
\n$ .envScriptsactivate<\/code><\/li>\n<\/ul>\nTo start the Flask development server, run the following command:<\/p>\n
$ flask run\n<\/code><\/pre>\nYou should see an output like this:<\/p>\n
* Running on http:\/\/127.0.0.1:5000\/ (Press CTRL+C to quit)\n<\/code><\/pre>\nOpen your web browser and navigate to `http:\/\/127.0.0.1:5000` to access the sentiment analysis application.<\/p>\n
Enter a piece of text in the provided textarea and click the “Analyze Sentiment” button. The sentiment analysis result will be displayed on the page.<\/p>\n
Congratulation! You have successfully built a sentiment analysis web application using OpenAI GPT-3 and Flask.<\/p>\n
Conclusion<\/h2>\n
In this tutorial, you learned how to build a sentiment analysis web application using OpenAI GPT-3 and Flask. We used OpenAI’s GPT-3 language model to perform sentiment analysis on user-provided text. Flask was used to create a simple web application that interacts with the OpenAI API. You can now take this application further by adding more features or integrating it with other services. Feel free to experiment and build upon what you have learned here. Happy coding!<\/p>\n","protected":false},"excerpt":{"rendered":"
Sentiment analysis is a powerful technique in natural language processing that helps determine the sentiment expressed in a piece of text. It involves analyzing the subjective information to classify the text into positive, negative, or neutral categories. In this tutorial, we will show you how to build a sentiment analysis Continue Reading<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[373,261,1824,95,391,41,40,1826,116,1825,975,1827,353,749,49],"yoast_head":"\nHow to Build a Sentiment Analysis App with OpenAI GPT-3 and Flask - Pantherax Blogs<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/localhost:10003\/how-to-build-a-sentiment-analysis-app-with-openai-gpt-3-and-flask\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Build a Sentiment Analysis App with OpenAI GPT-3 and Flask\" \/>\n<meta property=\"og:description\" content=\"Sentiment analysis is a powerful technique in natural language processing that helps determine the sentiment expressed in a piece of text. 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