Update app.py

app.py

@@ -1,17 +1,19 @@
 import gradio as gr
-import whisper
+from faster_whisper import WhisperModel
 from transformers import pipeline
 
-# Load models
+# Load Faster-Whisper and BART model
 print("Loading models...")
-whisper_model = whisper.load_model("base")
+whisper_model = WhisperModel("base")  # You can use "tiny" if it's still too heavy
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
 def transcribe(audio_path):
     if audio_path is None:
         return "Please record some audio."
-    result = whisper_model.transcribe(audio_path)
-    return result["text"]
+    
+    segments, _ = whisper_model.transcribe(audio_path)
+    transcription = " ".join([segment.text for segment in segments])
+    return transcription
 
 def summarize(text):
     if not text.strip():
@@ -19,11 +21,12 @@ def summarize(text):
     summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
     return summary[0]['summary_text']
 
+# Build Gradio UI
 with gr.Blocks() as app:
-    gr.Markdown("## 🎙️ Real-Time Transcription & Summarization Tool\nSpeak into your mic and generate a summary.")
+    gr.Markdown("## 🎙️ Real-Time Transcription & Summarization\nSpeak into your mic and generate a summary.")
 
     with gr.Row():
-        audio_input = gr.Audio(type="filepath", label="🎧 Record or Upload Audio")
+        audio_input = gr.Audio(type="filepath", label="🎧 Record Audio (Max ~120 sec)")
         transcription_output = gr.Textbox(label="📝 Transcription", lines=6, interactive=False)
 
     transcribe_button = gr.Button("Transcribe")