Used hugging face hub instead of torch and other modules to use the inference, instead of installing modules

app.py

@@ -1,16 +1,14 @@
 import gradio as gr
-from transformers import pipeline
+from huggingface_hub import InferenceClient
 
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base")
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+client = InferenceClient()
 
 
 def transcribe(audio):
     if audio is None:
         return "", ""
-    result = transcriber(audio)
-    text = result["text"]
-    return text, ""
+    text = client.automatic_speech_recognition(audio, model="openai/whisper-base")
+    return text.text, ""
 
 
 def summarize(text):
@@ -18,8 +16,8 @@ def summarize(text):
         return "No text to summarize."
     if len(text.split()) < 30:
         return "Text is too short to summarize."
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
-    return summary[0]["summary_text"]
+    result = client.summarization(text, model="facebook/bart-large-cnn")
+    return result.summary_text
 
 
 with gr.Blocks(title="Audio Transcription & Summary") as demo:

requirements.txt

@@ -1,3 +0,0 @@
-transformers
-torch
-gradio