Update app.py

app.py

@@ -1,171 +1,14 @@
-import torch
-import time
-import moviepy.editor as mp
-import psutil
 import gradio as gr
-import spaces
-from transformers import pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
-
-DEFAULT_MODEL_NAME = "distil-whisper/distil-large-v3"
-BATCH_SIZE = 8
-
-device = 0 if torch.cuda.is_available() else "cpu"
-if device == "cpu":
-    DEFAULT_MODEL_NAME = "openai/whisper-tiny"
-
-def load_pipeline(model_name):
-    return pipeline(
-        task="automatic-speech-recognition",
-        model=model_name,
-        chunk_length_s=30,
-        device=device,
-    )
-
-pipe = load_pipeline(DEFAULT_MODEL_NAME)
-
-@spaces.GPU
-def transcribe(inputs, task, model_name):
-    if inputs is None:
-        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-
-    global pipe
-    if model_name != pipe.model.name_or_path:
-        pipe = load_pipeline(model_name)
-
-    start_time = time.time()  # Record the start time
-
-    # Load the audio file and calculate its duration
-    audio = mp.AudioFileClip(inputs)
-    audio_duration = audio.duration
-
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    end_time = time.time()  # Record the end time
-
-    transcription_time = end_time - start_time  # Calculate the transcription time
-
-    # Create the transcription time output with additional information
-    transcription_time_output = (
-        f"Transcription Time: {transcription_time:.2f} seconds\n"
-        f"Audio Duration: {audio_duration:.2f} seconds\n"
-        f"Model Used: {model_name}\n"
-        f"Device Used: {'GPU' if torch.cuda.is_available() else 'CPU'}"
-    )
-
-    return text, transcription_time_output
-
-from gpustat import GPUStatCollection
-
-def update_gpu_status():
-    if torch.cuda.is_available() == False:
-        return "No Nviadia Device"
-    try:
-        gpu_stats = GPUStatCollection.new_query()
-        for gpu in gpu_stats:
-            # Assuming you want to monitor the first GPU, index 0
-            gpu_id = gpu.index
-            gpu_name = gpu.name
-            gpu_utilization = gpu.utilization
-            memory_used = gpu.memory_used
-            memory_total = gpu.memory_total
-            memory_utilization = (memory_used / memory_total) * 100
-            gpu_status=(f"GPU {gpu_id}: {gpu_name}, Utilization: {gpu_utilization}%, Memory Used: {memory_used}MB, Memory Total: {memory_total}MB, Memory Utilization: {memory_utilization:.2f}%")
-            return gpu_status
-
-    except Exception as e:
-        print(f"Error getting GPU stats: {e}")
-        return torch_update_gpu_status()
-
-def torch_update_gpu_status():
-    if torch.cuda.is_available():
-        gpu_info = torch.cuda.get_device_name(0)
-        gpu_memory = torch.cuda.mem_get_info(0)
-        total_memory = gpu_memory[1] / (1024 * 1024)
-        used_memory = (gpu_memory[1] - gpu_memory[0]) / (1024 * 1024)
-        
-        gpu_status = f"GPU: {gpu_info}\nTotal Memory: {total_memory:.2f} MB\nUsed Memory: {used_memory:.2f} MB"
-    else:
-        gpu_status = "No GPU available"
-    return gpu_status
-
-def update_cpu_status():
-    import datetime
-    # Get the current time
-    current_time = datetime.datetime.now().time()
-    # Convert the time to a string
-    time_str = current_time.strftime("%H:%M:%S")
-
-    cpu_percent = psutil.cpu_percent()
-    cpu_status = f"CPU Usage: {cpu_percent}% {time_str}"
-    return cpu_status
-
-def update_status():
-    gpu_status = update_gpu_status()
-    cpu_status = update_cpu_status()
-    return gpu_status, cpu_status
-
-def refresh_status():
-    return update_status()
+import numpy as np
+import time
 
-demo = gr.Blocks()
 
-mf_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.Audio(type="filepath"),
-        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
-        gr.Textbox(
-            label="Model Name",
-            value=DEFAULT_MODEL_NAME,
-            placeholder="Enter the model name",
-            info="Some available models: distil-whisper/distil-large-v3 distil-whisper/distil-medium.en Systran/faster-distil-whisper-large-v3 Systran/faster-whisper-large-v3 Systran/faster-whisper-medium openai/whisper-tiny, openai/whisper-base, openai/whisper-medium, openai/whisper-large-v3",
-        ),
-    ],
-    outputs=[gr.TextArea(label="Transcription"), gr.TextArea(label="Transcription Info")],
-    theme="huggingface",
-    title="Whisper Transcription",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the specified OpenAI Whisper"
-        " checkpoint and 🤗 Transformers to transcribe audio files of arbitrary length."
-    ),
-    allow_flagging="never",
-)
+graudio=gr.Audio(type="filepath",show_download_button=True)
 
-file_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.Audio(type="filepath", label="Audio file"),
-        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
-        gr.Textbox(
-            label="Model Name",
-            value=DEFAULT_MODEL_NAME,
-            placeholder="Enter the model name",
-            info="Some available models: openai/whisper-tiny, openai/whisper-base, openai/whisper-medium, openai/whisper-large-v2",
-        ),
-    ],
-    outputs=[gr.TextArea(label="Transcription"), gr.TextArea(label="Transcription Info")],
-    theme="huggingface",
-    title="Whisper Transcription",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the specified OpenAI Whisper"
-        " checkpoint and 🤗 Transformers to transcribe audio files of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-with demo:
-    gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
-    
-    with gr.Row():
-        refresh_button = gr.Button("Refresh Status")  # Create a refresh button
-    
-    gpu_status_output = gr.Textbox(label="GPU Status", interactive=False)
-    cpu_status_output = gr.Textbox(label="CPU Status", interactive=False)
-    
-    # Link the refresh button to the refresh_status function
-    refresh_button.click(refresh_status, None, [gpu_status_output, cpu_status_output])
 
-    # Load the initial status using update_status function
-    demo.load(update_status, inputs=None, outputs=[gpu_status_output, cpu_status_output], every=2, queue=False)
+demo = gr.Interface(fake_diffusion,
+                    inputs=[graudio],
+                    outputs="image")
 
-# Launch the Gradio app
-demo.launch(share=True)
+if __name__ == "__main__":
+    demo.launch()