Update app.py

app.py

@@ -1,51 +1,15 @@
-import streamlit as st
-import logging
-from transformers import pipeline
-
-# Setup logging
-logging.basicConfig(level=logging.INFO)
-
-# Load speech recognition pipeline
-asr = pipeline(task="automatic-speech-recognition",
-               model="facebook/wav2vec2-base-960h")
-
-# Function for transcribing speech
-def transcribe_speech(audio_file):
-    if not audio_file:
-        logging.error("No audio file provided.")
-        return "No audio found, please retry."
-    try:
-        logging.info(f"Processing file: {audio_file.name}")
-        audio_input = audio_file.read()
-        output = asr(audio_input)
-        return output[0]['transcription']
-    except Exception as e:
-        logging.error(f"Error during transcription: {str(e)}")
-        return f"Error processing the audio file: {str(e)}"
-
-# Streamlit app UI
-def main():
-    st.title("Simple Speech Recognition App")
-    st.write("### This app allows you to upload audio and see its transcription.")
-    
-    # Upload audio file
-    audio_file = st.file_uploader("Upload Audio File", type=["wav", "mp3"])
-    if audio_file:
-        st.audio(audio_file, format='audio/wav')
-
-    # Button to transcribe audio
-    if st.button("Transcribe Audio"):
-        if audio_file:
-            transcription = transcribe_speech(audio_file)
-            st.write("### Transcription:")
-            st.write(transcription)
-        else:
-            st.warning("Please upload an audio file first.")
-
-if __name__ == "__main__":
-    main()
-
-
+import numpy as np
+
+def image_classifier(inp):
+  confidence_scores = np.random.rand(3)
+  confidence_scores/= np.sum(confidence_scores)
+  classes= ['cable','case', 'cpu']
+  result= {classes[i]: confidence_scores[i] for i in range(3)}
+  return result
+
+import gradio as gr
+demo = gr.Interface(fn=image_classifier, inputs = "image", outputs = "label")
+demo.launch()