Update app.py

app.py

@@ -68,27 +68,35 @@ def transcribe(microphone, file_upload):
     
     file = microphone if microphone is not None else file_upload
     start_time = time.time()
-
     
     #--------------____________________________________________--------------"
+
     device = "cuda" if torch.cuda.is_available() else "cpu"
     pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large", device=device)
-    # chunk_length_s=30, generate_kwargs={'task': 'transcribe', 'language': 'no'}
     text = pipe(file)["text"]
+    
     #--------------____________________________________________--------------"
 
-    
     end_time = time.time()
     output_time = end_time - start_time
     word_count = len(text.split())
 
     # --GPU metrics
     memory = psutil.virtual_memory()
-    gpu_utilization, gpu_memory = GPUInfo.gpu_usage()
-    gpu_utilization = gpu_utilization[0] if len(gpu_utilization) > 0 else 0
-    gpu_memory = gpu_memory[0] if len(gpu_memory) > 0 else 0
+    
+    # Default GPU utilization and memory to 0 in case of an error
+    gpu_utilization = 0
+    gpu_memory = 0
+    try:
+        gpu_utilization, gpu_memory = GPUInfo.gpu_usage()
+        gpu_utilization = gpu_utilization[0] if len(gpu_utilization) > 0 else 0
+        gpu_memory = gpu_memory[0] if len(gpu_memory) > 0 else 0
+    except Exception as e:
+        print(f"Error retrieving GPU info: {e}")
+
     # --CPU metric
     cpu_usage = psutil.cpu_percent(interval=1)
+    
     # --system info string
     system_info = f"""
     *Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB.*
@@ -98,7 +106,7 @@ def transcribe(microphone, file_upload):
     *CPU Usage: {cpu_usage}%*
     """
 
-    return warn_output + text, system_info
+    return text, system_info
 
 def save_to_pdf(text, summary):
     pdf = FPDF()
@@ -143,7 +151,6 @@ with iface:
         with gr.Column():
             system_info = gr.Textbox(label="System Info")
 
-
     with gr.Tabs():
         with gr.TabItem("Download PDF"):
             pdf_text_only = gr.Button("Download PDF with Transcribed Text")
@@ -151,7 +158,19 @@ with iface:
 
             pdf_text_only.click(fn=lambda text: save_to_pdf(text, ""), inputs=[text_output], outputs=[pdf_output])
 
-
+    with gr.Row():
+        gr.Markdown('''
+        <div align="center">
+            <a href="https://opensource.com/resources/what-open-source">
+                <img src="https://badgen.net/badge/Open%20Source%20%3F/Yes%21/blue?icon=github" alt="Open Source? Yes!">
+        </a>
+        <span style="display:inline-block; width: 20px;"></span> 
+        <a href="https://opensource.org/licenses/Apache-2.0">
+            <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="License: Apache 2.0">
+        </a>
+        </div>
+        ''')
+        
     transcribe_btn.click(fn=transcribe, inputs=[microphone, upload], outputs=[text_output, system_info])