deployed mood prediction model with flask

.gitignore

@@ -1,2 +1,3 @@
 .DS_Store
 backend/.DS_Store
+backend/models/bert-mood-prediction-1.pt

app.py

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+from flask import Flask, request, jsonify, send_from_directory
+from lyricsgenius import Genius
+
+import torch
+import numpy as np
+from transformers import BertTokenizer, BertForSequenceClassification
+
+app = Flask(__name__)
+
+mood_map = {
+    0: 'Angry',
+    1: 'Happy',
+    3: 'Sad',
+    2: 'Relaxed'
+}
+
+# Load your pre-trained model and tokenizer
+model = BertForSequenceClassification.from_pretrained(
+    "bert-base-uncased", # Use the 12-layer BERT model, with an uncased vocab.
+    num_labels = 4, # The number of output labels.
+    output_attentions = False, # Whether the model returns attentions weights.
+    output_hidden_states = False, # Whether the model returns all hidden-states.
+)
+model.load_state_dict(torch.load('backend/models/bert-mood-prediction-1.pt', map_location=torch.device('cpu')))
+model.eval()
+
+def tokenize_and_format(sentences):
+  tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
+
+  # Tokenize all of the sentences and map the tokens to thier word IDs.
+  input_ids = []
+  attention_masks = []
+
+  # For every sentence...
+  for sentence in sentences:
+      # `encode_plus` will:
+      #   (1) Tokenize the sentence.
+      #   (2) Prepend the `[CLS]` token to the start.
+      #   (3) Append the `[SEP]` token to the end.
+      #   (4) Map tokens to their IDs.
+      #   (5) Pad or truncate the sentence to `max_length`
+      #   (6) Create attention masks for [PAD] tokens.
+      encoded_dict = tokenizer.encode_plus(
+                          sentence,                      # Sentence to encode.
+                          add_special_tokens = True, # Add '[CLS]' and '[SEP]'
+                          max_length = 256,           # Pad & truncate all sentences.
+                          padding = 'max_length',
+                          truncation = True,
+                          return_attention_mask = True,   # Construct attn. masks.
+                          return_tensors = 'pt',     # Return pytorch tensors.
+                    )
+
+      # Add the encoded sentence to the list.
+      input_ids.append(encoded_dict['input_ids'])
+
+      # And its attention mask (simply differentiates padding from non-padding).
+      attention_masks.append(encoded_dict['attention_mask'])
+  return torch.cat(input_ids, dim=0), torch.cat(attention_masks, dim=0)
+
+def get_prediction(iids, ams):
+    with torch.no_grad():
+        # Forward pass, calculate logit predictions.
+        outputs = model(iids,token_type_ids=None,
+                        attention_mask=ams)
+    logits = outputs.logits.detach().numpy()
+    pred_flat = np.argmax(logits, axis=1).flatten()
+    return pred_flat[0]
+
+def classify_lyrics(lyrics):
+    input_ids, attention_masks = tokenize_and_format([lyrics.replace('\n', ' ')])
+    prediction = get_prediction(input_ids, attention_masks)
+    mood = ["angry", "happy", "relaxed", "sad"][prediction]
+    return mood
+
+@app.route('/')
+def index():
+    return send_from_directory('frontend', 'index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.get_json()
+    song_title = data['title']
+    artist_name = data['artist']
+    success, lyrics = get_lyrics(song_title, artist_name)
+    if success:
+        mood = classify_lyrics(lyrics)
+        return jsonify({'mood': mood, 'lyrics': lyrics})
+    return jsonify({'mood': '-', 'lyrics': lyrics})
+
+def get_lyrics(song_title, artist_name):
+    # Implement the lyrics fetching logic here
+    # This is a placeholder function
+    token='PFl5Jdd01ayEMNqxIkuoAWnA7N9Xw9KqD9BSphLmjQ4IBrJqyaTA9CxKP2k8yJpz'
+    genius = Genius(token)
+    genius.timeout = 300
+    try:
+        song = genius.search_song(song_title, artist_name)
+        if song == None:
+            return False, f"Song not found - {song_title} by {artist_name}"
+        lyrics=song.lyrics
+        if lyrics.count('-')>200:
+            return False, f"Song not found - {song_title} by {artist_name}"
+        verses=[]
+        for x in lyrics.split('Lyrics')[1][:-7].split('\n'):
+            if '[' in list(x) or len(x)==0:
+                continue
+            verses.append(x.replace("\'","'"))
+        return True, '\n'.join(verses)
+    except TimeoutError:
+        return False, "TIMEOUT"
+
+if __name__ == '__main__':
+    app.run(debug=True)

frontend/index.html

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+<!DOCTYPE html>
+<html>
+<head>
+    <title>Lyrics Mood Predictor</title>
+</head>
+<body>
+    <h1>Enter Song Title and Artist</h1>
+    <form id="mood-form">
+        <input type="text" id="title" placeholder="Song Title" required>
+        <input type="text" id="artist" placeholder="Artist" required>
+        <button type="submit">Predict Mood</button>
+    </form>
+    <h2 id="mood-result"></h2>
+    <pre id="lyrics"></pre>
+
+    <script>
+        document.getElementById('mood-form').addEventListener('submit', async function(event) {
+            event.preventDefault();
+            const title = document.getElementById('title').value;
+            const artist = document.getElementById('artist').value;
+            
+            const response = await fetch('/predict', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ title, artist }),
+            });
+            const data = await response.json();
+            document.getElementById('mood-result').innerText = `Predicted Mood: ${data.mood}`;
+            document.getElementById('lyrics').innerText = data.lyrics;
+        });
+    </script>
+</body>
+</html>