Update app.py

app.py

@@ -1,22 +1,19 @@
-import whisper  # Ensure 'openai-whisper' is installed
-import gradio as gr
-import subprocess
-
-# Load the Whisper model
-try:
-    model = whisper.load_model("large-v3")  # Official Whisper model
-except Exception as e:
-    print(f"Error loading Whisper model: {e}")
-    raise e
-
 def transcribe_video(video_path):
     try:
+        # Debug: Check if the video file exists
+        if not os.path.exists(video_path):
+            return f"Error: File not found at {video_path}"
+
         # Extract audio from the uploaded video
         audio_path = "audio.wav"
-        subprocess.run(
+        process = subprocess.run(
             ["ffmpeg", "-i", video_path, "-ar", "16000", "-ac", "1", "-c:a", "pcm_s16le", audio_path],
-            check=True
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True
         )
+        if process.returncode != 0:
+            return f"ffmpeg error: {process.stderr}"
 
         # Transcribe the audio in Urdu
         result = model.transcribe(audio_path, task="transcribe", language="ur")
@@ -26,16 +23,3 @@ def transcribe_video(video_path):
         return "Error: ffmpeg is not installed or not found in the environment."
     except Exception as e:
         return f"An error occurred: {e}"
-
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=transcribe_video,
-    inputs=gr.Video(label="Upload your Urdu-speaking video"),
-    outputs=gr.Textbox(label="Transcribed Text"),
-    title="Urdu Video Transcription App",
-    description="Upload a video file in Urdu, and this app will transcribe the speech into text using Whisper.",
-)
-
-# Launch the app
-if __name__ == "__main__":
-    interface.launch()