Update app.py

app.py

@@ -163,6 +163,100 @@
 
 # demo.launch()
 
+# import gradio as gr
+# import os
+# import wave
+# import tempfile
+# import numpy as np
+
+# # Global variables to store file and line index
+# file_index = 0
+# line_index = 0
+# lines = []
+
+# # Hugging Face token and dataset saver
+# HF_TOKEN = os.getenv('HF_TOKEN')
+# hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-calculator-demo")
+
+# # Function to read lines from a file
+# def read_lines_from_file(file_path):
+#     global lines
+#     with open(file_path, 'r') as file:
+#         lines = file.readlines()
+
+# # Function to save audio to a WAV file
+# def save_audio_to_file(audio):
+#     sample_rate, data = audio  # audio is a tuple (sample_rate, data)
+    
+#     # Save the audio data as a WAV file in a temporary location
+#     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+#         with wave.open(tmp_file.name, 'wb') as wav_file:
+#             wav_file.setnchannels(1)  # Mono audio
+#             wav_file.setsampwidth(2)  # 2 bytes per sample (16-bit PCM)
+#             wav_file.setframerate(sample_rate)
+#             wav_file.writeframes(data.tobytes())
+        
+#         # Return the path to the saved WAV file
+#         return tmp_file.name
+
+# # Function to save data to the Hugging Face dataset
+# def save_to_hf_dataset(text, audio_path):
+#     with open(audio_path, "rb") as f:
+#         audio_data = f.read()
+#     hf_writer.save({"text": text, "audio": audio_data})
+
+# # Gradio interface function
+# def audio_capture_interface():
+#     global file_index, line_index, lines
+    
+#     # Initial file to read
+#     files = os.listdir('./audio_samples')
+#     read_lines_from_file(os.path.join('./audio_samples', files[file_index]))
+
+#     # Define the interface components
+#     audio_input = gr.Audio(source="microphone", type="numpy", label="Speak and click submit")
+#     output_text = gr.Textbox(label="Status", placeholder="Status will appear here")
+
+#     # Function to capture and process the audio input
+#     def process_audio(audio):
+#         global line_index, lines
+        
+#         try:
+#             text_line = lines[line_index].strip()
+#             file_path = save_audio_to_file(audio)
+#             save_to_hf_dataset(text_line, file_path)
+#             return f"Audio saved to {file_path} and uploaded to Hugging Face Dataset."
+#         except Exception as e:
+#             return f"Error saving audio: {str(e)}"
+
+#     # Function to handle navigation buttons
+#     def navigate_lines(button):
+#         global line_index, lines
+        
+#         if button == 'forward':
+#             line_index = min(line_index + 1, len(lines) - 1)
+#         elif button == 'previous':
+#             line_index = max(line_index - 1, 0)
+        
+#         output_text.value = lines[line_index]
+
+#     # Create the Gradio interface
+#     with gr.Blocks() as iface:
+#         with gr.Row():
+#             gr.Textbox(label="Text", value=lines[line_index], interactive=False)
+#         with gr.Row():
+#             audio_input.render()
+#         with gr.Row():
+#             gr.Button("Previous").click(lambda: navigate_lines('previous'), outputs=output_text)
+#             gr.Button("Forward").click(lambda: navigate_lines('forward'), outputs=output_text)
+#             gr.Button("Submit").click(process_audio, inputs=audio_input, outputs=output_text)
+
+#     return iface
+
+# # Launch the interface
+# iface = audio_capture_interface()
+# iface.launch()
+
 import gradio as gr
 import os
 import wave
@@ -178,6 +272,9 @@ lines = []
 HF_TOKEN = os.getenv('HF_TOKEN')
 hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-calculator-demo")
 
+# Ensure the directory exists
+os.makedirs('./audio_samples', exist_ok=True)
+
 # Function to read lines from a file
 def read_lines_from_file(file_path):
     global lines
@@ -209,8 +306,11 @@ def save_to_hf_dataset(text, audio_path):
 def audio_capture_interface():
     global file_index, line_index, lines
     
-    # Initial file to read
-    files = os.listdir('./audio_samples')
+    # Check for files in the directory
+    files = [f for f in os.listdir('./audio_samples') if os.path.isfile(os.path.join('./audio_samples', f))]
+    if not files:
+        return gr.Interface(fn=lambda: "No text files found in the directory.", inputs=None, outputs="text")
+    
     read_lines_from_file(os.path.join('./audio_samples', files[file_index]))
 
     # Define the interface components
@@ -238,17 +338,17 @@ def audio_capture_interface():
         elif button == 'previous':
             line_index = max(line_index - 1, 0)
         
-        output_text.value = lines[line_index]
+        return lines[line_index].strip()
 
     # Create the Gradio interface
     with gr.Blocks() as iface:
         with gr.Row():
-            gr.Textbox(label="Text", value=lines[line_index], interactive=False)
+            text_display = gr.Textbox(label="Text", value=lines[line_index].strip(), interactive=False)
         with gr.Row():
             audio_input.render()
         with gr.Row():
-            gr.Button("Previous").click(lambda: navigate_lines('previous'), outputs=output_text)
-            gr.Button("Forward").click(lambda: navigate_lines('forward'), outputs=output_text)
+            gr.Button("Previous").click(lambda: navigate_lines('previous'), None, text_display)
+            gr.Button("Forward").click(lambda: navigate_lines('forward'), None, text_display)
             gr.Button("Submit").click(process_audio, inputs=audio_input, outputs=output_text)
 
     return iface
@@ -256,4 +356,3 @@ def audio_capture_interface():
 # Launch the interface
 iface = audio_capture_interface()
 iface.launch()
-