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requirements.txt

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+gradio
+numpy
+librosa
+scikit-learn
+joblib
+requests

song.py

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+# -*- coding: utf-8 -*-
+"""SONG.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1D_uRTl2aO65nt7ABxgODlb57aLqY05HC
+"""
+
+import gradio as gr
+import numpy as np
+import librosa
+import os
+import requests
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report
+import joblib  # to save/load model
+
+# === CONFIGURATION ===
+SAAVN_SEARCH_URL = "https://saavn.dev/api/search/songs?query={query}&limit=1"
+DATA_DIR = "/content/drive/MyDrive/audio"  # update this if your folder is different
+
+# Create data directory if it doesn't exist
+if not os.path.exists(DATA_DIR):
+    os.makedirs(DATA_DIR)
+    print(f"Created directory: {DATA_DIR}. Please upload your audio dataset into this folder.")
+    # You might want to exit or handle this case where data is missing
+
+# === FEATURE EXTRACTION ===
+def extract_features(file_path):
+    try:
+        audio, sr = librosa.load(file_path, duration=3, offset=0.5)
+        mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
+        return np.mean(mfccs.T, axis=0)
+    except Exception as e:
+        print("Error loading file:", e)
+        return None
+
+# === LOAD DATA AND TRAIN MODEL ===
+def train_model(data_dir):
+    features = []
+    labels = []
+    for folder in os.listdir(data_dir):
+        emotion = folder.split('_')[-1].lower()
+        folder_path = os.path.join(data_dir, folder)
+        for filename in os.listdir(folder_path):
+            if filename.endswith('.wav'):
+                file_path = os.path.join(folder_path, filename)
+                mfcc = extract_features(file_path)
+                if mfcc is not None:
+                    features.append(mfcc)
+                    labels.append(emotion)
+
+    # Check if any data was loaded
+    if not features:
+        print(f"No audio files found in {data_dir}. Please upload your dataset.")
+        return None # Or raise an error
+
+    X = np.array(features)
+    y = np.array(labels)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = RandomForestClassifier()
+    model.fit(X_train, y_train)
+
+    # Optional: Save model
+    joblib.dump(model, "voice_mood_model.pkl")
+
+    # Evaluate (Optional)
+    y_pred = model.predict(X_test)
+    print("✅ Model Trained - Accuracy:", accuracy_score(y_test, y_pred))
+    print(classification_report(y_test, y_pred))
+    return model
+
+# Load existing or train new model
+model = None # Initialize model to None
+if os.path.exists("voice_mood_model.pkl"):
+    try:
+        model = joblib.load("voice_mood_model.pkl")
+        print("Loaded existing model.")
+    except Exception as e:
+        print(f"Error loading model: {e}. Retraining model.")
+        model = train_model(DATA_DIR)
+else:
+    model = train_model(DATA_DIR)
+
+# Ensure model is trained before proceeding
+if model is None:
+    print("Model could not be trained or loaded. Exiting.")
+    # You might want to add sys.exit() here if running as a script
+else:
+    # === PREDICT EMOTION FROM AUDIO ===
+    def predict_emotion(file_path):
+        mfcc = extract_features(file_path)
+        if mfcc is not None:
+            # Ensure the model is available before predicting
+            if model:
+                return model.predict(mfcc.reshape(1, -1))[0]
+            else:
+                print("Model not loaded or trained.")
+                return "Error: Model not available"
+        return "Unknown"
+
+    # === GET SONG FROM SAAVN ===
+    def get_song_from_mood(mood, language="tamil"):
+        query = f"{mood} {language} song"
+        url = SAAVN_SEARCH_URL.format(query=query)
+        try:
+            response = requests.get(url)
+            response.raise_for_status() # Raise an exception for bad status codes
+            data = response.json()
+            if data.get("data") and data["data"]["results"]:
+                song = data["data"]["results"][0]
+                title = song["name"]
+                artist = (
+                    song.get("artists", {}).get("primary", [{}])[0].get("name") or
+                    song.get("primaryArtists") or song.get("artist") or "Unknown Artist"
+                )
+                return f"🎶 **{title}** by *{artist}*\n🔗 [Listen here]({song['url']})"
+        except requests.exceptions.RequestException as e:
+            print("Song fetch error:", e)
+        except Exception as e:
+            print("Song fetch error:", e)
+        return "😕 No song found for this mood."
+
+    # === GRADIO INTERFACE FUNCTION ===
+    def detect_and_recommend(audio_path):
+        if model: # Check if model is available
+            mood = predict_emotion(audio_path)
+            song = get_song_from_mood(mood)
+            return f"🎭 Detected Mood: **{mood}**\n\n{song}"
+        else:
+            return "Model not trained or loaded. Cannot detect mood."
+
+
+    # === LAUNCH GRADIO APP ===
+    gr.Interface(
+        fn=detect_and_recommend,
+        inputs=gr.Audio(type="filepath", label="🎙 Upload Your Voice"),
+        outputs="markdown",
+        title="🎤 Voice2Vibes (Offline Version)",
+        description="Detects mood from your voice using your dataset and recommends a matching Tamil song 🎶"
+    ).launch()

song_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d06471fcb1488261721e062d218a7480b4af23043de51fff14343bdc89f37df4
+size 1168089