Delete app .py

app .py

@@ -1,70 +0,0 @@
-import gradio as gr
-import whisper    # Library for speech recognition
-from transformers import pipeline
-import pandas as pd
-
-
-# Load the Whisper model for speech recognition
-whisper_model = whisper.load_model("base")
-
-# Load the summarization model from Hugging Face
-summarization = pipeline("summarization", model="google/pegasus-large")
-
-def process_audio(audio_file, min_length, max_length):
-    try:
-        # Ensure audio_file is not None and has valid content
-        if audio_file is None:
-            raise ValueError("No audio file provided.")
-
-        # Use the Whisper model to transcribe the audio file into text
-        result = whisper_model.transcribe(audio_file)
-        text = result['text']
-
-        # Check if transcription was successful
-        if not text:
-            raise ValueError("Failed to transcribe the audio. The transcription result is empty.")
-
-        # Use the summarization pipeline to summarize the transcribed text
-        summary_result = summarization(text, min_length=min_length, max_length=max_length)
-        summary = summary_result[0]['summary_text']
-
-        # Check if summarization was successful
-        if not summary:
-            raise ValueError("Failed to summarize the transcript. The summary result is empty.")
-
-        # Create a DataFrame to store the audio file, transcript, and summary
-        df_results = pd.DataFrame({
-            "Audio File": [audio_file],  # Store the path to the audio file
-            "Transcript": [text],       # Store the transcribed text
-            "Summary": [summary]        # Store the generated summary
-        })
-
-        # Save the results to a CSV file named "results.csv"
-        df_results.to_csv("results.csv", index=False)
-
-        # Return the transcript and summary to be displayed in the Gradio interface
-        return text, summary
-
-    except Exception as e:
-        # General error handling
-        error_message = f"An error occurred: {str(e)}"
-        return error_message, error_message
-
-# Create a Gradio interface
-iface = gr.Interface(
-    fn=process_audio,  # The function to be called when processing the input
-    inputs=[
-        gr.Audio(sources="upload", type="filepath", label="Upload your audio file"),  # Audio input field for file upload
-        gr.Slider(minimum=10, maximum=50, value=30, label="Minimum Summary Length"),   # Slider for setting minimum summary length
-        gr.Slider(minimum=50, maximum=600, value=100, label="Maximum Summary Length")  # Slider for setting maximum summary length
-    ],
-    outputs=[
-        gr.Textbox(label="Transcript"),  # Textbox for displaying the transcript
-        gr.Textbox(label="Summary")      # Textbox for displaying the summary
-    ],
-    title="Audio to Summarized Transcript",  # Title of the app
-    description="Upload an audio file and adjust summary length to get both the transcript and summary."  # Description of the app
-)
-
-# Launch the app
-iface.launch()