Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,58 +2,54 @@ import os
 
 os.environ["TRANSFORMERS_CACHE"] = "/app/.cache"
 os.environ["HF_HOME"] = "/app/.cache"
+import os
 import torch
 import torchaudio
+import tempfile
 from transformers import WhisperProcessor, WhisperForConditionalGeneration
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import streamlit as st
 
-
-def load_models():
-    whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
-    whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
-    text_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
-    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    return whisper_processor, whisper_model, text_model, tokenizer
-
-whisper_processor, whisper_model, text_model, tokenizer = load_models()
-
-def transcribe(audio_path):
-    waveform, sample_rate = torchaudio.load(audio_path)
-    input_features = whisper_processor(
-        waveform.squeeze().numpy(),
-        sampling_rate=sample_rate,
-        return_tensors="pt"
-    ).input_features
+whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
+whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
+text_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+
+def transcribe(audio_bytes):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+        tmp.write(audio_bytes)
+        tmp_path = tmp.name
+    waveform, sample_rate = torchaudio.load(tmp_path)
+    input_features = whisper_processor(waveform.squeeze().numpy(), sampling_rate=sample_rate, return_tensors="pt").input_features
     predicted_ids = whisper_model.generate(input_features)
     transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+    os.remove(tmp_path)
     return transcription
 
 def extract_text_features(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     outputs = text_model(**inputs)
-    predicted_class = outputs.logits.argmax(dim=1).item()
-    return "Hate Speech" if predicted_class == 1 else "Not Hate Speech"
-
-def predict(audio_file, text_input):
-    if not audio_file and not text_input:
-        return "Please provide either an audio file or some text."
-    if audio_file is not None:
-        audio_path = "temp_audio.wav"
-        with open(audio_path, "wb") as f:
-            f.write(audio_file.read())
-        transcribed_text = transcribe(audio_path)
-        prediction = extract_text_features(text_input or transcribed_text)
-        return f"Predicted: {prediction} \n\n(Transcribed: {transcribed_text})" if not text_input else f"Predicted: {prediction}"
-    elif text_input:
-        prediction = extract_text_features(text_input)
-        return f"Predicted: {prediction}"
-
-st.title("Hate Speech Detector")
-
-uploaded_audio = st.file_uploader("Upload Audio File (.mp3, .wav, .ogg, .flac, .opus)", type=["mp3", "wav", "ogg", "flac", "opus"])
-text_input = st.text_input("Or enter text:")
+    return outputs.logits.argmax(dim=1).item()
+
+def predict_hate_speech(audio_bytes, text):
+    if audio_bytes:
+        transcription = transcribe(audio_bytes)
+        text_input = text if text else transcription
+    elif text:
+        text_input = text
+    else:
+        return "Please provide audio or text"
+    prediction = extract_text_features(text_input)
+    return "Hate Speech" if prediction == 1 else "Not Hate Speech"
+
+st.title("Hate Speech Detection")
+audio_file = st.file_uploader("Upload audio file", type=["wav", "mp3", "flac", "ogg", "opus"])
+text_input = st.text_input("Or enter text")
 
 if st.button("Predict"):
-    result = predict(uploaded_audio, text_input)
-    st.success(result)
+    if audio_file is not None or text_input:
+        audio_bytes = audio_file.read() if audio_file else None
+        result = predict_hate_speech(audio_bytes, text_input)
+        st.success(result)
+    else:
+        st.warning("Please provide either audio or text input")