├── Models
    ├── scaler.pkl
    ├── model_dt.pkl
    ├── model_rf.pkl
    ├── model_xgb.pkl
    └── countVectorizer.pkl
├── Sentiment-Analysis.zip
├── requirements.txt
├── README.md
├── Data
    ├── SentimentBulk.csv
    └── Predictions.csv
├── main.py
├── templates
    ├── index.html
    └── landing.html
└── api.py


/Models/scaler.pkl:
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https://raw.githubusercontent.com/pik1989/Sentiment-Analysis/HEAD/Models/scaler.pkl


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/Models/model_dt.pkl:
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https://raw.githubusercontent.com/pik1989/Sentiment-Analysis/HEAD/Models/model_dt.pkl


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/Models/model_rf.pkl:
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https://raw.githubusercontent.com/pik1989/Sentiment-Analysis/HEAD/Models/model_rf.pkl


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/Models/model_xgb.pkl:
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https://raw.githubusercontent.com/pik1989/Sentiment-Analysis/HEAD/Models/model_xgb.pkl


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/Sentiment-Analysis.zip:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/pik1989/Sentiment-Analysis/HEAD/Sentiment-Analysis.zip


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/Models/countVectorizer.pkl:
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https://raw.githubusercontent.com/pik1989/Sentiment-Analysis/HEAD/Models/countVectorizer.pkl


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/requirements.txt:
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1 | numpy
2 | matplotlib
3 | seaborn
4 | scikit-learn
5 | wordcloud
6 | nltk
7 | xgboost
8 | streamlit
9 | flask


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/README.md:
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 1 | # Amazon-Alexa-Reviews
 2 | 
 3 | ## Watch Video: https://www.youtube.com/watch?v=6A2w-KYG4Ko
 4 | 
 5 | ## HOW TO RUN
 6 | 
 7 | Step 1: Clone the repository
 8 | ```
 9 | git clone https://github.com/Surbhit01/Amazon-Alexa-Reviews.git
10 | ```
11 | 
12 | Step 2: Open the cloned repository and create a conda environment. Activate the new environment
13 | ```
14 | conda create -n amazonreview python=3.10
15 | ```
16 | ```
17 | conda activate amazonreview
18 | ```
19 | 
20 | Step 3: Install the requirements file
21 | ```
22 | pip install -r requirements.txt
23 | ```
24 | 
25 | Step 4: Run the app
26 | ```
27 | flask --app api.py run
28 | ```
29 | 
30 | Step 5: The app will run on port 5000. 
31 | ```
32 | localhost:5000
33 | ```
34 | ## NOTE: The issue raised is fixed, please download the .zip folder and run it.
35 | 


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/Data/SentimentBulk.csv:
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1 | Sentence
2 | Love my Echo!
3 | Loved it!
4 | "Sometimes while playing a game, you can answer a question correctly but Alexa says you got it wrong and answers the same as you.  I like being able to turn lights on and off while away from home."
5 | "I have had a lot of fun with this thing. My 4 yr old learns about dinosaurs, i control the lights and play games like categories. Has nice sound when playing music as well."
6 | "This item did not work. Certified refurbished should mean it works as advertised. Instead this item crashed as soon as I turned it on and plugged it in. When trying to connect from my phone to the echo dot, it crashed, over and over. Not only would it disconnect but the orange light would freeze and then the thing would reboot with the blue light. Alexa would tell me it’s ready to connect and freeze mid sentence. Tried holding the action button for 5 seconds and it didn’t do anything. Returning immediately and hope they actually fix this item when the next buyer purchases it."
7 | Does not work all the time
8 | I've already returned it.


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/Data/Predictions.csv:
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1 | Sentence,Predicted sentiment
2 | Love my Echo!,Positive
3 | Loved it!,Positive
4 | "Sometimes while playing a game, you can answer a question correctly but Alexa says you got it wrong and answers the same as you.  I like being able to turn lights on and off while away from home.",Negative
5 | "I have had a lot of fun with this thing. My 4 yr old learns about dinosaurs, i control the lights and play games like categories. Has nice sound when playing music as well.",Positive
6 | "This item did not work. Certified refurbished should mean it works as advertised. Instead this item crashed as soon as I turned it on and plugged it in. When trying to connect from my phone to the echo dot, it crashed, over and over. Not only would it disconnect but the orange light would freeze and then the thing would reboot with the blue light. Alexa would tell me it’s ready to connect and freeze mid sentence. Tried holding the action button for 5 seconds and it didn’t do anything. Returning immediately and hope they actually fix this item when the next buyer purchases it.",Positive
7 | Does not work all the time,Negative
8 | I've already returned it.,Negative
9 | 


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/main.py:
--------------------------------------------------------------------------------
 1 | import streamlit as st
 2 | import pandas as pd
 3 | import requests
 4 | from io import BytesIO
 5 | 
 6 | # flask --app api.py run --port=5000
 7 | prediction_endpoint = "http://127.0.0.1:5000/predict"
 8 | 
 9 | st.title("Text Sentiment Predictor")
10 | 
11 | uploaded_file = st.file_uploader(
12 |     "Choose a CSV file for bulk prediction - Upload the file and click on Predict",
13 |     type="csv",
14 | )
15 | 
16 | # Text input for sentiment prediction
17 | user_input = st.text_input("Enter text and click on Predict", "")
18 | 
19 | # Prediction on single sentence
20 | if st.button("Predict"):
21 |     if uploaded_file is not None:
22 |         file = {"file": uploaded_file}
23 |         response = requests.post(prediction_endpoint, files=file)
24 |         response_bytes = BytesIO(response.content)
25 |         response_df = pd.read_csv(response_bytes)
26 | 
27 |         st.download_button(
28 |             label="Download Predictions",
29 |             data=response_bytes,
30 |             file_name="Predictions.csv",
31 |             key="result_download_button",
32 |         )
33 | 
34 |     else:
35 |         response = requests.post(prediction_endpoint, data={"text": user_input})
36 |         response = response.json()
37 |         st.write(f"Predicted sentiment: {response['prediction']}")
38 | 


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/templates/index.html:
--------------------------------------------------------------------------------
  1 | <!DOCTYPE html>
  2 | <html lang="en">
  3 | 
  4 | <head>
  5 |     <meta charset="UTF-8">
  6 |     <meta http-equiv="X-UA-Compatible" content="IE=edge">
  7 |     <meta name="viewport" content="width=device-width, initial-scale=1.0">
  8 |     <title>Text Sentiment Predictor</title>
  9 | </head>
 10 | 
 11 | <body>
 12 |     <h1>Text Sentiment Prediction</h1>
 13 | 
 14 |     <form id="predictionForm">
 15 |         <input type="file" id="csvFileInput" accept=".csv">
 16 |         <textarea id="textInput" placeholder="Enter text..."></textarea>
 17 |         <button type="button" onclick="predict()">Predict</button>
 18 |         <button id="downloadBtn" style="display:none" onclick="downloadPredictions()">Download Predictions</button>
 19 |     </form>
 20 | 
 21 |     <div id="predictionResult"></div>
 22 |     <div id="graphContainer"></div>
 23 | 
 24 |     <script>
 25 |         function predict() {
 26 |             // Check if CSV file is present
 27 |             var csvFileInput = document.getElementById("csvFileInput");
 28 |             var textInput = document.getElementById("textInput");
 29 |             var predictionResult = document.getElementById("predictionResult");
 30 |             var graphContainer = document.getElementById("graphContainer");
 31 | 
 32 |             if (csvFileInput.files.length > 0) {
 33 |                 // Upload CSV file
 34 |                 var formData = new FormData();
 35 |                 formData.append("file", csvFileInput.files[0]);
 36 | 
 37 |                 fetch("http://localhost:5000/predict", {
 38 |                     method: "POST",
 39 |                     body: formData
 40 |                 })
 41 |                     .then(response => {
 42 |                         if (response.headers.get('X-Graph-Exists') === 'true') {
 43 |                             console.log("Graph")
 44 |                             var graphData = response.headers.get('X-Graph-Data');
 45 |                             displayGraph(graphData);
 46 |                         }
 47 | 
 48 |                         return response.blob();
 49 |                     })
 50 |                     .then(blob => {
 51 |                         console.log("Blob:", blob);
 52 | 
 53 |                         document.getElementById("downloadBtn").style.display = "block";
 54 |                         document.getElementById("downloadBtn").onclick = function () {
 55 |                             console.log("Downloading...");
 56 |                             var url = URL.createObjectURL(blob);
 57 |                             console.log("URL:", url);
 58 | 
 59 |                             var a = document.createElement("a");
 60 |                             a.href = url;
 61 |                             a.download = "Predictions.csv";
 62 |                             document.body.appendChild(a);
 63 |                             a.click();
 64 |                             document.body.removeChild(a);
 65 |                         };
 66 |                     })
 67 |                     .catch(error => {
 68 |                         console.error("Error:", error);
 69 |                     });
 70 | 
 71 |             } else if (textInput.value.trim() !== "") {
 72 |                 // Predict on single sentence
 73 |                 fetch("http://localhost:5000/predict", {
 74 |                     method: "POST",
 75 |                     headers: {
 76 |                         "Content-Type": "application/json"
 77 |                     },
 78 |                     body: JSON.stringify({ "text": textInput.value.trim() })
 79 |                 })
 80 |                     .then(response => response.json())
 81 |                     .then(data => {
 82 |                         console.log(data)
 83 |                         predictionResult.innerHTML = "Predicted sentiment: " + data.prediction;
 84 |                     });
 85 |             }
 86 |         }
 87 | 
 88 |         function downloadPredictions() {
 89 |             console.log("Download prediction")
 90 |         }
 91 | 
 92 |         function displayGraph(graphData) {
 93 |             var graphUrl = "data:image/png;base64," + graphData;
 94 |             var img = document.createElement('img');
 95 |             img.src = graphUrl;
 96 |             graphContainer.appendChild(img);
 97 |         }
 98 |     </script>
 99 | 
100 | </body>
101 | 
102 | </html>


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/api.py:
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  1 | from flask import Flask, request, jsonify, send_file, render_template
  2 | import re
  3 | from io import BytesIO
  4 | 
  5 | # nltk.download('stopwords')
  6 | from nltk.corpus import stopwords
  7 | from nltk.stem.porter import PorterStemmer
  8 | import matplotlib.pyplot as plt
  9 | import pandas as pd
 10 | import pickle
 11 | import base64
 12 | 
 13 | STOPWORDS = set(stopwords.words("english"))
 14 | 
 15 | app = Flask(__name__)
 16 | 
 17 | 
 18 | @app.route("/test", methods=["GET"])
 19 | def test():
 20 |     return "Test request received successfully. Service is running."
 21 | 
 22 | 
 23 | @app.route("/", methods=["GET", "POST"])
 24 | def home():
 25 |     return render_template("landing.html")
 26 | 
 27 | 
 28 | @app.route("/predict", methods=["POST"])
 29 | def predict():
 30 |     # Select the predictor to be loaded from Models folder
 31 |     predictor = pickle.load(open(r"Models/model_xgb.pkl", "rb"))
 32 |     scaler = pickle.load(open(r"Models/scaler.pkl", "rb"))
 33 |     cv = pickle.load(open(r"Models/countVectorizer.pkl", "rb"))
 34 |     try:
 35 |         # Check if the request contains a file (for bulk prediction) or text input
 36 |         if "file" in request.files:
 37 |             # Bulk prediction from CSV file
 38 |             file = request.files["file"]
 39 |             data = pd.read_csv(file)
 40 | 
 41 |             predictions, graph = bulk_prediction(predictor, scaler, cv, data)
 42 | 
 43 |             response = send_file(
 44 |                 predictions,
 45 |                 mimetype="text/csv",
 46 |                 as_attachment=True,
 47 |                 download_name="Predictions.csv",
 48 |             )
 49 | 
 50 |             response.headers["X-Graph-Exists"] = "true"
 51 | 
 52 |             response.headers["X-Graph-Data"] = base64.b64encode(
 53 |                 graph.getbuffer()
 54 |             ).decode("ascii")
 55 | 
 56 |             return response
 57 | 
 58 |         elif "text" in request.json:
 59 |             # Single string prediction
 60 |             text_input = request.json["text"]
 61 |             predicted_sentiment = single_prediction(predictor, scaler, cv, text_input)
 62 | 
 63 |             return jsonify({"prediction": predicted_sentiment})
 64 | 
 65 |     except Exception as e:
 66 |         return jsonify({"error": str(e)})
 67 | 
 68 | 
 69 | def single_prediction(predictor, scaler, cv, text_input):
 70 |     corpus = []
 71 |     stemmer = PorterStemmer()
 72 |     review = re.sub("[^a-zA-Z]", " ", text_input)
 73 |     review = review.lower().split()
 74 |     review = [stemmer.stem(word) for word in review if not word in STOPWORDS]
 75 |     review = " ".join(review)
 76 |     corpus.append(review)
 77 |     X_prediction = cv.transform(corpus).toarray()
 78 |     X_prediction_scl = scaler.transform(X_prediction)
 79 |     y_predictions = predictor.predict_proba(X_prediction_scl)
 80 |     y_predictions = y_predictions.argmax(axis=1)[0]
 81 | 
 82 |     return "Positive" if y_predictions == 1 else "Negative"
 83 | 
 84 | 
 85 | def bulk_prediction(predictor, scaler, cv, data):
 86 |     corpus = []
 87 |     stemmer = PorterStemmer()
 88 |     for i in range(0, data.shape[0]):
 89 |         review = re.sub("[^a-zA-Z]", " ", data.iloc[i]["Sentence"])
 90 |         review = review.lower().split()
 91 |         review = [stemmer.stem(word) for word in review if not word in STOPWORDS]
 92 |         review = " ".join(review)
 93 |         corpus.append(review)
 94 | 
 95 |     X_prediction = cv.transform(corpus).toarray()
 96 |     X_prediction_scl = scaler.transform(X_prediction)
 97 |     y_predictions = predictor.predict_proba(X_prediction_scl)
 98 |     y_predictions = y_predictions.argmax(axis=1)
 99 |     y_predictions = list(map(sentiment_mapping, y_predictions))
100 | 
101 |     data["Predicted sentiment"] = y_predictions
102 |     predictions_csv = BytesIO()
103 | 
104 |     data.to_csv(predictions_csv, index=False)
105 |     predictions_csv.seek(0)
106 | 
107 |     graph = get_distribution_graph(data)
108 | 
109 |     return predictions_csv, graph
110 | 
111 | 
112 | def get_distribution_graph(data):
113 |     fig = plt.figure(figsize=(5, 5))
114 |     colors = ("green", "red")
115 |     wp = {"linewidth": 1, "edgecolor": "black"}
116 |     tags = data["Predicted sentiment"].value_counts()
117 |     explode = (0.01, 0.01)
118 | 
119 |     tags.plot(
120 |         kind="pie",
121 |         autopct="%1.1f%%",
122 |         shadow=True,
123 |         colors=colors,
124 |         startangle=90,
125 |         wedgeprops=wp,
126 |         explode=explode,
127 |         title="Sentiment Distribution",
128 |         xlabel="",
129 |         ylabel="",
130 |     )
131 | 
132 |     graph = BytesIO()
133 |     plt.savefig(graph, format="png")
134 |     plt.close()
135 | 
136 |     return graph
137 | 
138 | 
139 | def sentiment_mapping(x):
140 |     if x == 1:
141 |         return "Positive"
142 |     else:
143 |         return "Negative"
144 | 
145 | 
146 | if __name__ == "__main__":
147 |     app.run(port=5000, debug=True)
148 | 


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/templates/landing.html:
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  1 | <!DOCTYPE html>
  2 | <html>
  3 | 
  4 | <head>
  5 |   <meta charset="utf-8" />
  6 |   <meta name="viewport" content="width=device-width, initial-scale=1" />
  7 |   <meta name="theme-color" content="#000000" />
  8 |   <link rel="shortcut icon" href="./assets/img/favicon.ico" />
  9 |   <link rel="apple-touch-icon" sizes="76x76" href="./assets/img/apple-icon.png" />
 10 |   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css" />
 11 |   <link rel="stylesheet"
 12 |     href="https://cdn.jsdelivr.net/gh/creativetimofficial/tailwind-starter-kit/compiled-tailwind.min.css" />
 13 |   <title>Amazon Alexa Reviews Analysis</title>
 14 | </head>
 15 | 
 16 | <body class="text-gray-800 antialiased">
 17 |   <script src="https://cdn.jsdelivr.net/gh/alpinejs/alpine@v2.x.x/dist/alpine.js" defer></script>
 18 | 
 19 |   <main>
 20 |     <div class="relative pt-16 pb-32 flex content-center items-center justify-center" style="min-height: 75vh;">
 21 |       <div class="absolute top-0 w-full h-full bg-center bg-cover"
 22 |         style='background-image: linear-gradient(90deg, rgba(17,223,169,1) 0%, rgba(3,60,88,1) 49%, rgba(10,32,50,1) 100%);'>
 23 |         <span id="blackOverlay" class="w-full h-full absolute opacity-50 bg-black"></span>
 24 |       </div>
 25 |       <div class="container relative mx-auto">
 26 |         <div class="items-center flex flex-wrap">
 27 |           <div class="w-full lg:w-6/12 px-4 ml-auto mr-auto text-center">
 28 |             <div class="pr-12">
 29 |               <h1 class="text-white font-semibold text-5xl">
 30 |                 Understand the emotions behind the words.😊
 31 |               </h1>
 32 |               <p class="mt-4 text-lg text-gray-300">
 33 |                 Text sentiment prediction is a powerful tool that can help you to understand the emotions and opinions
 34 |                 expressed in your text data.
 35 |                 This information can be used to improve your business in a number of ways
 36 |               </p>
 37 |             </div>
 38 |           </div>
 39 |         </div>
 40 |       </div>
 41 |       <div class="top-auto bottom-0 left-0 right-0 w-full absolute pointer-events-none overflow-hidden"
 42 |         style="height: 70px;">
 43 |         <svg class="absolute bottom-0 overflow-hidden" xmlns="http://www.w3.org/2000/svg" preserveAspectRatio="none"
 44 |           version="1.1" viewBox="0 0 2560 100" x="0" y="0">
 45 |           <polygon class="text-gray-300 fill-current" points="2560 0 2560 100 0 100"></polygon>
 46 |         </svg>
 47 |       </div>
 48 |     </div>
 49 | 
 50 |     <section class="relative py-20">
 51 |       <div class="bottom-auto top-0 left-0 right-0 w-full absolute pointer-events-none overflow-hidden -mt-20"
 52 |         style="height: 80px;">
 53 |         <svg class="absolute bottom-0 overflow-hidden" xmlns="http://www.w3.org/2000/svg" preserveAspectRatio="none"
 54 |           version="1.1" viewBox="0 0 2560 100" x="0" y="0">
 55 |           <polygon class="text-white fill-current" points="2560 0 2560 100 0 100"></polygon>
 56 |         </svg>
 57 |       </div>
 58 |       <div class="flex flex-col items-center justify-center">
 59 |         <h1 class="text-5xl font-semibold  py-2 ">Text Sentiment Prediction</h1>
 60 |       </div>
 61 |       <div class="container mx-auto px-4 py-24">
 62 | 
 63 |         <div class="items-center flex flex-wrap">
 64 |           <div class="w-full md:w-4/12 ml-auto mr-auto px-4">
 65 |             <form id="predictionForm">
 66 | 
 67 | 
 68 | 
 69 | 
 70 |               <div class="container max-w-full mx-auto md:py-24 px-6">
 71 |                 <div class="max-w-sm mx-auto px-6">
 72 |                   <div class="relative flex flex-wrap">
 73 |                     <div class="w-full relative">
 74 |                       <div class="md:mt-6">
 75 |                         <form class="mt-8" id="predictionForm">
 76 |                           <div class="mx-auto max-w-lg ">
 77 |                             <div class="py-1">
 78 |                               <span class="px-1 text-sm text-gray-600">Upload your CSV file</span>
 79 |                               <input placeholder="" type="file" id="csvFileInput" accept=".csv"
 80 |                                 class="text-md block px-3 py-2 rounded-lg w-full
 81 |                                    bg-white border-2 border-gray-300 placeholder-gray-600 shadow-md focus:placeholder-gray-500 focus:bg-white focus:border-gray-600 focus:outline-none">
 82 |                             </div>
 83 | 
 84 |                             <div class="py-1">
 85 |                               <span class="px-1 text-sm text-gray-600">Text for Prediction</span>
 86 |                               <textarea
 87 |                                 class="text-md block px-3 py-2 rounded-lg w-full 
 88 |                                 bg-white border-2 border-gray-300 placeholder-gray-600 shadow-md focus:placeholder-gray-500 focus:bg-white focus:border-gray-600 focus:outline-none"
 89 |                                 id="textInput" placeholder="Enter text..."></textarea>
 90 | 
 91 |                             </div>
 92 | 
 93 |                             <button type="button" onclick="predict()" class="mt-3 text-lg font-semibold
 94 |                                                             bg-gray-800 w-full text-white rounded-lg
 95 |                                                      px-6 py-3 block shadow-xl hover:text-white hover:bg-black">
 96 |                               Predict
 97 |                             </button>
 98 | 
 99 |                           </div>
100 |                         </form>
101 | 
102 | 
103 | 
104 |                       </div>
105 |                     </div>
106 |                   </div>
107 |                 </div>
108 |               </div>
109 |             </form>
110 |           </div>
111 |           <div class="w-full md:w-4/12 ml-auto mr-auto px-4">
112 |             <div>
113 |               <h1 class="text-4xl underline underline-offset-8  ">Prediction Result</h1>
114 |               <div class="p-4 m-2 border">
115 |                 <div id="predictionResult"></div>
116 | 
117 |               </div>
118 |             </div>
119 |             <div class="pt-6">
120 |               <h1 class="text-4xl underline underline-offset-8  ">graph Result</h1>
121 |               <div class="p-4 m-2 border  ">
122 |                 <div id="graphContainer"></div>
123 |               </div>
124 |             </div>
125 |             <button id="downloadBtn" style="display:none" onclick="downloadPredictions()" class="mt-3 text-lg font-semibold
126 |             bg-gray-800 w-full text-white rounded-lg
127 |      px-6 py-3 mt-10 block shadow-xl hover:text-white hover:bg-black">
128 |               Download Predictions
129 |             </button>
130 |           </div>
131 | 
132 |         </div>
133 |       </div>
134 |     </section>
135 | 
136 | 
137 | 
138 |   </main>
139 |   <footer class="relative bg-gray-900 pt-8 pb-6">
140 |     <div class="bottom-auto top-0 left-0 right-0 w-full absolute pointer-events-none overflow-hidden -mt-20"
141 |       style="height: 80px;">
142 |       <svg class="absolute bottom-0 overflow-hidden" xmlns="http://www.w3.org/2000/svg" preserveAspectRatio="none"
143 |         version="1.1" viewBox="0 0 2560 100" x="0" y="0">
144 |         <polygon class="text-gray-300 fill-current" points="2560 0 2560 100 0 100"></polygon>
145 |       </svg>
146 |     </div>
147 |     <div class="container mx-auto px-4">
148 | 
149 |       <hr class="my-6 border-gray-400" />
150 |       <div class="flex flex-wrap items-center md:justify-between justify-center">
151 |         <div class="w-full md:w-4/12 px-4 mx-auto text-center">
152 |           <div class="text-sm text-white font-semibold py-1">
153 |             Copyright © Text sentiment prediction
154 | 
155 |           </div>
156 |         </div>
157 |       </div>
158 |     </div>
159 |   </footer>
160 | </body>
161 | <script>
162 |   function predict() {
163 |     // Check if CSV file is present
164 |     var csvFileInput = document.getElementById("csvFileInput");
165 |     var textInput = document.getElementById("textInput");
166 |     var predictionResult = document.getElementById("predictionResult");
167 |     var graphContainer = document.getElementById("graphContainer");
168 | 
169 |     if (csvFileInput.files.length > 0) {
170 |       // Upload CSV file
171 |       var formData = new FormData();
172 |       formData.append("file", csvFileInput.files[0]);
173 | 
174 |       fetch("http://localhost:5000/predict", {
175 |         method: "POST",
176 |         body: formData
177 |       })
178 |         .then(response => {
179 |           if (response.headers.get('X-Graph-Exists') === 'true') {
180 |             console.log("Graph")
181 |             var graphData = response.headers.get('X-Graph-Data');
182 |             displayGraph(graphData);
183 |           }
184 | 
185 |           return response.blob();
186 |         })
187 |         .then(blob => {
188 |           console.log("Blob:", blob);
189 | 
190 |           document.getElementById("downloadBtn").style.display = "block";
191 |           document.getElementById("downloadBtn").onclick = function () {
192 |             console.log("Downloading...");
193 |             var url = URL.createObjectURL(blob);
194 |             console.log("URL:", url);
195 | 
196 |             var a = document.createElement("a");
197 |             a.href = url;
198 |             a.download = "Predictions.csv";
199 |             document.body.appendChild(a);
200 |             a.click();
201 |             document.body.removeChild(a);
202 |           };
203 |         })
204 |         .catch(error => {
205 |           console.error("Error:", error);
206 |         });
207 | 
208 |     } else if (textInput.value.trim() !== "") {
209 |       // Predict on single sentence
210 |       fetch("http://localhost:5000/predict", {
211 |         method: "POST",
212 |         headers: {
213 |           "Content-Type": "application/json"
214 |         },
215 |         body: JSON.stringify({ "text": textInput.value.trim() })
216 |       })
217 |         .then(response => response.json())
218 |         .then(data => {
219 |           console.log(data)
220 |           predictionResult.innerHTML = "Predicted sentiment: " + data.prediction;
221 |         });
222 |     }
223 |   }
224 | 
225 |   function downloadPredictions() {
226 |     console.log("Download prediction")
227 |   }
228 | 
229 |   function displayGraph(graphData) {
230 |     predictionResult.innerHTML = "";
231 |     var graphUrl = "data:image/png;base64," + graphData;
232 |     var img = document.createElement('img');
233 |     img.src = graphUrl;
234 |     graphContainer.appendChild(img);
235 |   }
236 | </script>
237 | 
238 | </html>


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