Update app.py

app.py

@@ -1,74 +1,80 @@
-# ======================
-# app.py
-# ======================
-
-from flask import Flask, request, jsonify
-import tensorflow as tf
-import numpy as np
-import pandas as pd
-import librosa
-from transformers import AutoTokenizer, TFAutoModel
-
-# Load saved files
-model = tf.keras.models.load_model("model.h5")
-scaler = pd.read_pickle("scaler.pkl")
-encoder = pd.read_pickle("label_encoder.pkl")
-meta = pd.read_excel("raga_metadata.xlsx")
-
-# Load IndicBERT model
-tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicBERTv2-MLM-only")
-bert_model = TFAutoModel.from_pretrained("ai4bharat/IndicBERTv2-MLM-only", from_pt=True)
-
-app = Flask(__name__)
-
-def extract_features(file_path):
-    y, sr = librosa.load(file_path, sr=22050)
-    features = {
-        "chroma_stft": np.mean(librosa.feature.chroma_stft(y=y, sr=sr)),
-        "spec_cent": np.mean(librosa.feature.spectral_centroid(y=y, sr=sr)),
-    }
-    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=18)
-    for i in range(18):
-        features[f"mfcc{i+1}"] = np.mean(mfccs[i])
-    return pd.DataFrame([features])
-
-def predict_raga(audio_df, raga_name):
-    audio_scaled = scaler.transform(audio_df)
-    audio_lstm_input = audio_scaled.reshape((1, 1, audio_scaled.shape[1]))
-
-    # Get description for this raga
-    description_text = meta[meta['raga'] == raga_name]['description'].values
-    if len(description_text) == 0:
-        description_text = [""]
-
-    desc_tok = tokenizer(description_text.tolist(), padding=True, truncation=True, max_length=64, return_tensors="tf")
-    desc_embed = bert_model(desc_tok['input_ids'], attention_mask=desc_tok['attention_mask'])[0][:, 0, :]
-
-    pred = model.predict([audio_lstm_input, desc_embed])
-    return encoder.inverse_transform([np.argmax(pred)])[0]
-
-@app.route("/")
-def home():
-    return "🎶 Raga Prediction API is Live!"
-
-@app.route("/predict", methods=["POST"])
-def predict():
-    try:
-        audio_file = request.files['audio']
-        raga_name = request.form['raga_name']
-
-        temp_audio_path = "temp_audio.wav"
-        audio_file.save(temp_audio_path)
-
-        features = extract_features(temp_audio_path)
-        predicted_raga = predict_raga(features, raga_name)
-
-        return jsonify({
-            "predicted_raga": predicted_raga
-        })
-
-    except Exception as e:
-        return jsonify({"error": str(e)})
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+# app.py
+import gradio as gr
+import pandas as pd
+import numpy as np
+import librosa
+import joblib
+import tensorflow as tf
+from keras.models import load_model
+from transformers import AutoTokenizer, TFAutoModel
+
+# ====================
+# 1. Load Model and Assets
+# ====================
+model = load_model("raga_predictor_model.h5")
+scaler = joblib.load("scaler.pkl")
+encoder = joblib.load("label_encoder.pkl")
+
+# Load tokenizer and BERT model directly from Hugging Face
+tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicBERTv2-MLM-only")
+bert_model = TFAutoModel.from_pretrained("ai4bharat/IndicBERTv2-MLM-only")
+
+# Load metadata
+meta = pd.read_csv("raga_metadata.csv")
+raga_descriptions = dict(zip(meta['raga'], meta['description']))
+
+# ====================
+# 2. Define Utility Functions
+# ====================
+def extract_features(file_path):
+    y, sr = librosa.load(file_path, sr=22050)
+    features = {
+        "chroma_stft": np.mean(librosa.feature.chroma_stft(y=y, sr=sr)),
+        "spec_cent": np.mean(librosa.feature.spectral_centroid(y=y, sr=sr)),
+    }
+    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=18)
+    for i in range(18):
+        features[f"mfcc{i+1}"] = np.mean(mfccs[i])
+    return pd.DataFrame([features])
+
+def tokenize_description(description_text):
+    desc_tok = tokenizer(description_text, padding=True, truncation=True, max_length=64, return_tensors="tf")
+    desc_embed = bert_model(desc_tok['input_ids'], attention_mask=desc_tok['attention_mask'])[0][:, 0, :]
+    return desc_embed
+
+def predict_raga(audio_file):
+    # Extract features
+    audio_df = extract_features(audio_file.name)
+    audio_scaled = scaler.transform(audio_df)
+    audio_lstm_input = audio_scaled.reshape((1, 1, audio_scaled.shape[1]))
+
+    # Use a dummy description
+    description_text = ""
+
+    # Tokenize dummy description
+    desc_embed = tokenize_description([description_text])
+
+    # Predict
+    pred = model.predict([audio_lstm_input, desc_embed])
+    raga_pred = encoder.inverse_transform([np.argmax(pred)])[0]
+
+    # Get description
+    description = raga_descriptions.get(raga_pred, "No description available.")
+
+    return f"🎵 Predicted Raga: {raga_pred}\n\n📝 Description:\n{description}"
+
+# ====================
+# 3. Gradio Interface
+# ====================
+title = "🎶 Raga Prediction App"
+description = "Upload an Indian classical music clip, and I will predict the Raga for you!"
+
+interface = gr.Interface(
+    fn=predict_raga,
+    inputs=gr.Audio(type="file", label="Upload Audio File"),
+    outputs="text",
+    title=title,
+    description=description,
+)
+
+interface.launch()