Update app.py

app.py

@@ -1,20 +1,20 @@
 import streamlit as st
-from transformers import pipeline
+import requests
 import os
 
-# Define your Hugging Face token; secure this appropriately
-HF_TOKEN = os.getenv('HF_TOKEN')
+# Hugging Face API setup
+API_URL = "https://api-inference.huggingface.co/models/MIT/ast-finetuned-audioset-10-10-0.4593"
+HF_TOKEN = os.getenv("HF_TOKEN")
+headers = {"Authorization": f"Bearer {HF_TOKEN}"} 
 
-# Set up Hugging Face pipeline for audio classification using the specified model
-model_name = "MIT/ast-finetuned-audioset-10-10-0.4593"
+# Function to send audio file to the Hugging Face model for classification
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL, headers=headers, files={"file": f})
+    return response.json()
 
-@st.cache(allow_output_mutation=True)
-def load_model(token, model_name):
-    return pipeline("audio-classification", model=model_name, use_auth_token=token)
-
-audio_classifier = load_model(HF_TOKEN, model_name)
-
-# Pre-uploaded audio files
+# Pre-uploaded audio files (assuming these files are stored in a directory named 'audio_files')
 audio_files = {
     "Labrador Barking": "labrador-barking.mp3",
     "Tolling Bell": "tolling-bell.mp3",
@@ -25,23 +25,22 @@ audio_files = {
 }
 
 # Streamlit UI
-st.title("Audio Classification with Pre-uploaded Files")
+st.title("Audio Classification with Hugging Face Inference API")
 
 # Audio file selection
 selected_audio_name = st.selectbox("Select an audio file", list(audio_files.keys()))
-audio_file_path = audio_files[selected_audio_name]
+audio_file_path = os.path.join("path_to_your_audio_files", audio_files[selected_audio_name])  # Update path as necessary
 
 # Perform classification
 if st.button("Classify"):
-    # Read audio file
-    with open(audio_file_path, "rb") as audio_file:
-        audio_bytes = audio_file.read()
-
-    results = audio_classifier(audio_bytes)
+    results = query(audio_file_path)
 
     # Displaying results
     st.write("Classification Results:")
-    for result in results:
-        label = result['label']
-        score = round(result['score'], 4)  # Adjust rounding as needed
-        st.write(f"Label: {label}, Score: {score}")
+    if isinstance(results, list):  # Check if the response is as expected
+        for result in results:
+            label = result['label']
+            score = round(result['score'], 4)  # Adjust rounding as needed
+            st.write(f"Label: {label}, Score: {score}")
+    else:
+        st.write("An error occurred:", results)