Update app.py

app.py

@@ -1,187 +1,52 @@
-# Copyright 2025 Xiaomi Corporation.
+# Updated for Inachi-Core (Elephant AI) - Text & Audio Dual Mode
 import os
 import time
-
 import gradio as gr
-import torch
-from huggingface_hub import snapshot_download
-
 from src.mimo_audio.mimo_audio import MimoAudio
 
-
-MODEL_REPO = "XiaomiMiMo/MiMo-V2.5-ASR"
-TOKENIZER_REPO = "XiaomiMiMo/MiMo-Audio-Tokenizer"
-DOWNLOAD_ROOT = os.environ.get("MIMO_DOWNLOAD_ROOT", "assets/models")
-
-LANGUAGE_TAGS = {
-    "Auto": "",
-    "Chinese": "<chinese>",
-    "English": "<english>",
-}
-
-
-def download_models():
-    os.makedirs(DOWNLOAD_ROOT, exist_ok=True)
-    hf_token = os.getenv("HF_TOKEN")
-
-    model_path = os.path.join(DOWNLOAD_ROOT, MODEL_REPO.replace("/", "_"))
-    tokenizer_path = os.path.join(DOWNLOAD_ROOT, TOKENIZER_REPO.replace("/", "_"))
-
-    print(f"[download] {MODEL_REPO} -> {model_path}")
-    snapshot_download(repo_id=MODEL_REPO, token=hf_token, local_dir=model_path)
-
-    print(f"[download] {TOKENIZER_REPO} -> {tokenizer_path}")
-    snapshot_download(repo_id=TOKENIZER_REPO, token=hf_token, local_dir=tokenizer_path)
-
-    return model_path, tokenizer_path
-
-
-class ASRGenerator:
-    def __init__(self, model):
-        self.model = model
-
-    def transcribe(self, audio_path, audio_tag=""):
-        return self.model.asr_sft(audio_path, audio_tag=audio_tag)
-
-
-class MiMoV25ASRInterface:
+class InachiProEngine:
     def __init__(self, model_path, tokenizer_path):
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        print(f"[init] device={device}")
-        print(f"[init] model_path={model_path}")
-        print(f"[init] tokenizer_path={tokenizer_path}")
-
+        # MiMo-V2.5-Pro load කිරීම
         self.model = MimoAudio(model_path, tokenizer_path)
-        self.asr_generator = ASRGenerator(self.model)
-        print("[init] model ready")
-
-    def transcribe(self, uploaded_audio, recorded_audio, language_choice):
-        audio_path = uploaded_audio or recorded_audio
-        if audio_path is None:
-            return "", "❌ Error: Please upload an audio file or record from your microphone."
 
+    def generate(self, text_input, audio_input, language_choice):
+        # Audio හෝ Text යන දෙකෙන් ඕනෑම එකක් process කිරීමේ හැකියාව
         audio_tag = LANGUAGE_TAGS.get(language_choice, "")
-
+        
         try:
-            print(f"Performing ASR task:")
-            print(f"  Audio: {audio_path}")
-            print(f"  Language: {language_choice} (tag='{audio_tag}')")
-
             start = time.time()
-            transcript = self.asr_generator.transcribe(audio_path, audio_tag=audio_tag)
+            # මෙතනදී text_input එක කෙලින්ම model එකට pass කළ හැකියි
+            # MiMo-Pro හි text-to-text හෝ audio-to-text functions පාවිච්චි වේ
+            if audio_input:
+                result = self.model.asr_sft(audio_input, audio_tag=audio_tag)
+            else:
+                # Text chat logic
+                result = self.model.chat(text_input) 
+            
             elapsed = time.time() - start
-
-            status_msg = (
-                f"✅ Transcription completed in {elapsed:.2f}s\n"
-                f"🎵 Input audio: {os.path.basename(audio_path)}\n"
-                f"🌐 Language tag: {language_choice}"
-            )
-            return transcript, status_msg
-
+            return result, f"🚀 Processed in {elapsed:.2f}s"
         except Exception as e:
-            error_msg = f"❌ Error during transcription: {str(e)}"
-            print(error_msg)
-            return "", error_msg
-
-    def create_interface(self):
-        with gr.Blocks(
-            title="MiMo-V2.5-ASR Speech Recognition",
-            theme=gr.themes.Soft(),
-            fill_height=True,
-            analytics_enabled=False,
-        ) as iface:
-            gr.Markdown("# 🎙️ MiMo-V2.5-ASR: Robust Speech Recognition")
-            gr.Markdown(
-                "Upload an audio file **or** record directly from your microphone. "
-                "Supports Chinese, English, Chinese dialects, code-switch, singing, "
-                "noisy environments, and multi-speaker scenarios."
-            )
-
-            with gr.Row():
-                with gr.Column():
-                    uploaded_audio = gr.Audio(
-                        label="Upload Audio File",
-                        type="filepath",
-                        sources=["upload"],
-                        interactive=True,
-                    )
-                    recorded_audio = gr.Audio(
-                        label="Or Record from Microphone",
-                        type="filepath",
-                        sources=["microphone"],
-                        interactive=True,
-                    )
-                    language_choice = gr.Radio(
-                        label="Language Tag",
-                        choices=list(LANGUAGE_TAGS.keys()),
-                        value="Auto",
-                        info=(
-                            "Auto: automatic language detection (recommended for "
-                            "code-switched speech). Select Chinese or English to "
-                            "bias the model toward that language."
-                        ),
-                    )
-                    transcribe_btn = gr.Button(
-                        "🎧 Transcribe", variant="primary", size="lg"
-                    )
-
-                with gr.Column():
-                    output_text = gr.Textbox(
-                        label="Transcription",
-                        lines=10,
-                        interactive=False,
-                        placeholder="Transcription result will appear here...",
-                        show_copy_button=True,
-                    )
-                    status = gr.Textbox(
-                        label="Status",
-                        lines=4,
-                        interactive=False,
-                        placeholder="Processing status will be shown here...",
-                    )
-                    with gr.Row():
-                        clear_btn = gr.Button("🗑️ Clear", size="sm")
-
-            transcribe_btn.click(
-                fn=self.transcribe,
-                inputs=[uploaded_audio, recorded_audio, language_choice],
-                outputs=[output_text, status],
-            )
-
-            def clear_all():
-                return None, None, "Auto", "", ""
-
-            clear_btn.click(
-                fn=clear_all,
-                outputs=[
-                    uploaded_audio,
-                    recorded_audio,
-                    language_choice,
-                    output_text,
-                    status,
-                ],
-            )
-
-        return iface
-
-
-def main():
-    print("🚀 Launch MiMo-V2.5-ASR demo...")
-
-    model_path, tokenizer_path = download_models()
-    interface = MiMoV25ASRInterface(model_path, tokenizer_path)
-
-    iface = interface.create_interface()
-
-    host = os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0")
-    port = int(os.environ.get("GRADIO_SERVER_PORT", "7898"))
-    print(f"🌐 Launch service - {host}:{port}")
-    iface.queue(default_concurrency_limit=4, max_size=20).launch(
-        server_name=host,
-        server_port=port,
-        show_api=False,
-    )
-
-
-if __name__ == "__main__":
-    main()
+            return "", f"❌ Error: {str(e)}"
+
+# UI එකට Textbox එකක් ඇතුළත් කිරීම
+def create_dual_interface(engine):
+    with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as iface:
+        gr.Markdown("# 🔱 INACHI-CORE | MiMo-V2.5-Pro")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                audio_in = gr.Audio(label="Audio Input (Optional)", type="filepath")
+                text_in = gr.Textbox(label="Message / Prompt", placeholder="Type your command here...")
+                lang = gr.Radio(choices=["Auto", "Chinese", "English"], value="Auto", label="Language Context")
+                submit_btn = gr.Button("Execute Command", variant="primary")
+            
+            with gr.Column(scale=1):
+                chat_out = gr.Textbox(label="Inachi Response", lines=12)
+                status = gr.Label(label="System Heartbeat")
+
+        submit_btn.click(
+            fn=engine.generate,
+            inputs=[text_in, audio_in, lang],
+            outputs=[chat_out, status]
+        )
+    return iface