Upload main.py

main.py

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+import os
+import io
+import tempfile
+import numpy as np
+import librosa
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+import tensorflow as tf
+
+app = FastAPI(title="AI Music Mood Categorizer")
+
+# 1. Define the 8 target moods matching our Flutter app
+MOODS = ["gym", "travel", "chill", "sleep", "love", "sad", "party", "study"]
+
+# 2. Load your TensorFlow Model (Placeholder)
+# In production, you would uncomment this and load your trained model:
+# model = tf.keras.models.load_model("mood_classifier.h5")
+
+def extract_features(audio_path):
+    """
+    Extracts audio features using librosa.
+    This takes the 15-second audio chunk sent by the Flutter app.
+    """
+    # Load audio with librosa
+    # sr=22050 is the default sample rate.
+    y, sr = librosa.load(audio_path, sr=22050)
+    
+    # Extract features
+    # MFCCs (Mel-frequency cepstral coefficients) describe the 'shape' of the sound
+    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    mfcc_mean = np.mean(mfcc.T, axis=0)
+    
+    # Chroma measures pitch/harmonic content (useful for 'sad' vs 'happy' chords)
+    chroma = librosa.feature.chroma_stft(y=y, sr=sr)
+    chroma_mean = np.mean(chroma.T, axis=0)
+    
+    # Spectral contrast (useful for distinguishing energetic music)
+    contrast = librosa.feature.spectral_contrast(y=y, sr=sr)
+    contrast_mean = np.mean(contrast.T, axis=0)
+
+    # Tempo (BPM)
+    tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
+    
+    # Combine all features into a single numpy array
+    # This is what your ML model will take as input for prediction
+    features = np.hstack([mfcc_mean, chroma_mean, contrast_mean, tempo])
+    return features, tempo[0]
+
+@app.post("/analyze")
+async def analyze_audio(file: UploadFile = File(...)):
+    """
+    Endpoint that receives the 15s audio chunk from Flutter, 
+    extracts features, runs the AI model, and returns the mood.
+    """
+    try:
+        # 1. Save the uploaded audio chunk to a temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
+            content = await file.read()
+            temp_audio.write(content)
+            temp_audio_path = temp_audio.name
+
+        # 2. Extract AI Features using Librosa
+        features, bpm = extract_features(temp_audio_path)
+        
+        # 3. Predict Mood using TensorFlow (Mock logic)
+        # In production, you would run:
+        # prediction = model.predict(np.array([features]))
+        # predicted_index = np.argmax(prediction)
+        # predicted_mood = MOODS[predicted_index]
+        
+        # --- MOCK HEURISTIC FOR TESTING ---
+        # Since you haven't trained the TFLite/TF model yet, 
+        # this heuristic uses the BPM (tempo) to mock a prediction 
+        # so you can test the Flutter app immediately!
+        predicted_mood = "chill"
+        if bpm > 140:
+            predicted_mood = "gym" # Aggressive, fast
+        elif bpm > 120:
+            predicted_mood = "party" # Upbeat
+        elif bpm > 100:
+            predicted_mood = "travel" # Moving
+        elif bpm < 70:
+            predicted_mood = "sleep" # Very slow, ambient
+        elif bpm < 90:
+            predicted_mood = "sad" # Slow, melancholic
+        # ----------------------------------
+
+        # Clean up temp file
+        os.remove(temp_audio_path)
+
+        return JSONResponse(content={
+            "mood": predicted_mood,
+            "bpm": float(bpm),
+            "status": "success"
+        })
+
+    except Exception as e:
+        return JSONResponse(
+            status_code=500,
+            content={"status": "error", "message": str(e)}
+        )
+
+# To run this server locally for testing:
+# pip install -r requirements.txt
+# uvicorn main:app --host 0.0.0.0 --port 8000