Update app.py

app.py

@@ -4,19 +4,24 @@ import torch
 import soundfile as sf
 from pathlib import Path
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel, get_peft_model, LoraConfig, TaskType
+from peft import PeftModel
 from snac import SNAC
 
 # -----------------------------
 # CONFIG
 # -----------------------------
-MODEL_NAME = "rahul7star/nava1.0"   # Base Maya model
-LORA_NAME  = "rahul7star/nava-audio"  # LoRA adapter
+MODEL_NAME = "rahul7star/nava1.0"
+LORA_NAME = "rahul7star/nava-audio"
 SEQ_LEN = 2048
 TARGET_SR = 24000
 OUT_ROOT = Path("/tmp/data")
 OUT_ROOT.mkdir(parents=True, exist_ok=True)
 
+DEFAULT_TEXT = (
+    "राजनीतिज्ञों ने कहा कि उन्होंने निर्णायक मत को अनावश्यक रूप से "
+    "निर्धारित करने के लिए अफ़गान संविधान में काफी अस्पष्टता पाई थी"
+)
+
 # -----------------------------
 # GENERATE AUDIO (LoRA)
 # -----------------------------
@@ -35,12 +40,12 @@ def generate_audio_cpu_lora(text: str):
         )
         logs.append("✅ Loaded base Maya model")
 
-        # Load LoRA adapter from HF Hub
+        # Load LoRA adapter
         model = PeftModel.from_pretrained(base_model, LORA_NAME, device_map={"": DEVICE_CPU})
         model.eval()
         logs.append(f"✅ Applied LoRA adapter from {LORA_NAME}")
 
-        # Build prompt: just text prompt
+        # Build prompt
         soh_token = tokenizer.decode([128259])
         eoh_token = tokenizer.decode([128260])
         soa_token = tokenizer.decode([128261])
@@ -99,28 +104,45 @@ def generate_audio_cpu_lora(text: str):
         sf.write(audio_path, audio, TARGET_SR)
         logs.append(f"✅ Audio saved: {audio_path}, duration: {len(audio)/TARGET_SR:.2f}s")
 
-        return str(audio_path), "\n".join(logs)
+        return str(audio_path), str(audio_path), "\n".join(logs)
 
     except Exception as e:
         import traceback
         logs.append(f"[❌] CPU LoRA TTS error: {e}\n{traceback.format_exc()}")
-        return None, "\n".join(logs)
+        return None, None, "\n".join(logs)
 
 # -----------------------------
 # GRADIO UI
 # -----------------------------
 with gr.Blocks() as demo:
     gr.Markdown("# Maya LoRA TTS (CPU)")
-    input_text = gr.Textbox(label="Enter text", lines=2, placeholder="Type Hindi text here...")
+
+    # Input text
+    input_text = gr.Textbox(label="Enter text", lines=2, value=DEFAULT_TEXT)
+
+    # Generate button
     run_button = gr.Button("🔊 Generate Audio")
-    audio_output = gr.Audio(label="Generated Audio", type="filepath")
+
+    # Outputs
+    audio_output = gr.Audio(label="Play Generated Audio", type="filepath")
+    download_output = gr.File(label="Download Audio")
     logs_output = gr.Textbox(label="Logs", lines=12)
 
     run_button.click(
         fn=generate_audio_cpu_lora,
         inputs=[input_text],
-        outputs=[audio_output, logs_output]
+        outputs=[audio_output, download_output, logs_output]
     )
 
+    # -----------------------------
+    # Example section
+    # -----------------------------
+    gr.Markdown("### Example")
+    example_text = DEFAULT_TEXT
+    example_audio_path = "/audio.wav"  # Placeholder for later upload
+
+    gr.Textbox(label="Example Text", value=example_text, lines=2, interactive=False)
+    gr.Audio(label="Example Audio", value=example_audio_path, type="filepath", interactive=False)
+
 if __name__ == "__main__":
     demo.launch()