Update app.py

app.py

@@ -1,31 +1,22 @@
 import os
-import subprocess
 import openai
 import gradio as gr
 from gtts import gTTS
-import torch
-import librosa
-from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
-
-# Load the processor and model
-processor = AutoProcessor.from_pretrained("lyimo/whisper-small-sw2")
-model = AutoModelForSpeechSeq2Seq.from_pretrained("lyimo/whisper-small-sw2")
+from transformers import pipeline
 
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
+pipe = pipeline(model="lyimo/whisper-small-sw2")  # Use your custom ASR model for transcription
+
 def transcribe(audio):
-    audio_data, rate = librosa.load(audio, sr=16000)
-    inputs = processor(audio_data, sampling_rate=rate, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model.generate(inputs["input_features"], max_length=512, num_beams=4, early_stopping=True)
-    transcription = [processor.decode(ids) for ids in outputs]
-    return transcription[0]
+    text = pipe(audio)["text"]
+    return text
 
 def generate_response(transcribed_text):
     response = openai.ChatCompletion.create(
         model="gpt-3.5-turbo",
         messages=[
-            {"role": "system", "content": " All your answers should be in swahili only, users undertands swahili only so here we start... Wewe ni mtaalamu wa viazi lishe na utajibu maswali yote kwa kiswahili tu!"},
+            {"role": "system", "content": "All your answers should be in swahili only, users undertands swahili only so here we start... Wewe ni mtaalamu wa viazi lishe na utajibu maswali yote kwa kiswahili tu!"},
             {"role": "user", "content": "Mambo vipi?"},
             {"role": "assistant", "content": "Salama je una swali lolote kuhusu viazi lishe?"},
             {"role": "user", "content": "nini maana ya Viazi lishe?"},