Add application file

app.py

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+import gradio as gr
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import librosa
+import numpy as np
+
+# -------- Load Model (small) --------
+MODEL_NAME = "distilgpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(MODEL_NAME)
+model = GPT2LMHeadModel.from_pretrained(MODEL_NAME)
+
+# -------- Session State --------
+class SessionState:
+    def __init__(self):
+        self.tempo = None
+        self.energy = None
+        self.lyrics = []
+
+state = SessionState()
+
+# -------- Beat Analysis (lightweight) --------
+def analyze_beat(audio):
+    y, sr = librosa.load(audio, sr=16000, mono=True)
+    tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
+    energy = float(np.mean(np.abs(y)))
+    return int(tempo), round(energy, 3)
+
+# -------- Generate Lyrics --------
+def generate_lines(mood, lines=4, regenerate=False):
+    global state
+
+    if state.tempo is None:
+        return "Upload a beat first."
+
+    # Remove last 2 lines if regenerating
+    if regenerate and len(state.lyrics) >= 2:
+        state.lyrics = state.lyrics[:-2]
+
+    context = "\n".join(state.lyrics)
+
+    prompt = (
+        f"{context}\n"
+        f"Rap lyrics for a {mood} beat at {state.tempo} BPM "
+        f"with energy {state.energy}:\n"
+    )
+
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+
+    output = model.generate(
+        inputs,
+        max_length=inputs.shape[1] + lines * 12,
+        do_sample=True,
+        temperature=0.9,
+        top_p=0.95,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    text = tokenizer.decode(output[0], skip_special_tokens=True)
+    new_part = text.replace(prompt, "").strip().split("\n")
+
+    clean_lines = [l.strip() for l in new_part if l.strip()][:lines]
+
+    state.lyrics.extend(clean_lines)
+
+    return "\n".join(state.lyrics)
+
+# -------- Upload Handler --------
+def handle_upload(audio):
+    global state
+    tempo, energy = analyze_beat(audio)
+    state.tempo = tempo
+    state.energy = energy
+    state.lyrics = []
+    return f"Beat analyzed: {tempo} BPM | Energy: {energy}"
+
+# -------- UI --------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎵 Beat-to-Lyrics Generator (Free CPU Optimized)")
+
+    audio_input = gr.Audio(type="filepath")
+    upload_btn = gr.Button("Analyze Beat")
+    beat_info = gr.Textbox(label="Beat Info")
+
+    mood = gr.Dropdown(
+        ["Chill", "Hype", "Trap", "Lo-fi", "Boom-bap"],
+        value="Chill",
+        label="Mood"
+    )
+
+    lyrics_output = gr.Textbox(lines=20, label="Lyrics")
+
+    generate_btn = gr.Button("Generate Lines")
+    regenerate_btn = gr.Button("Regenerate Last 2 Lines")
+
+    upload_btn.click(handle_upload, audio_input, beat_info)
+    generate_btn.click(lambda m: generate_lines(m, 6, False), mood, lyrics_output)
+    regenerate_btn.click(lambda m: generate_lines(m, 4, True), mood, lyrics_output)
+
+demo.launch()