Create app.py

app.py

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+import streamlit as st
+from faster_whisper import WhisperModel
+from transformers import pipeline
+from textblob import TextBlob
+import numpy as np
+import queue
+import threading
+import time
+
+# Initialize shared state
+audio_queue = queue.Queue()
+transcription_results = []
+ai_detection_results = []
+
+# Global stop event for threads
+stop_event = threading.Event()
+
+def initialize_model():
+    """Initialize Whisper model and AI detectors."""
+    st.session_state.model = WhisperModel(model_size="small", device="cpu", compute_type="int8")
+    st.session_state.ai_detector = pipeline('text-classification', model='roberta-base-openai-detector')
+
+def advanced_ai_detection(text, ai_detector):
+    """Perform AI detection on the text."""
+    if len(text.split()) < 5:  # Skip short texts
+        return "Insufficient Data"
+
+    result = ai_detector(text)[0]
+    return result
+
+def analyze_text_linguistics(text):
+    """Perform linguistic analysis on the text."""
+    blob = TextBlob(text)
+    return {
+        "sentiment": blob.sentiment.polarity,
+        "subjectivity": blob.sentiment.subjectivity
+    }
+
+def transcribe_audio(audio_chunk, model):
+    """Transcribe audio using the Whisper model."""
+    segments, _ = model.transcribe(audio_chunk, language="en")
+    return [segment.text for segment in segments]
+
+def run_app():
+    """Main Streamlit app function."""
+    st.title("AI Speech Detector")
+    st.subheader("Real-Time Speech Transcription and AI Detection")
+    st.text("This app transcribes audio input and detects if the text is AI-generated.")
+
+    # Sidebar for controls
+    st.sidebar.title("Controls")
+    start_button = st.sidebar.button("Start Recording")
+    stop_button = st.sidebar.button("Stop Recording")
+
+    if "model" not in st.session_state:
+        st.text("Loading AI models...")
+        initialize_model()
+        st.text("Models loaded successfully!")
+
+    # Display transcript
+    st.text_area("Real-Time Transcript", height=200, key="transcript")
+
+    # Display AI detection results
+    st.text_area("AI Detection Results", height=200, key="ai_detection")
+
+    if start_button:
+        st.session_state.is_recording = True
+        threading.Thread(target=process_audio_stream).start()
+
+    if stop_button:
+        st.session_state.is_recording = False
+        stop_event.set()
+
+def process_audio_stream():
+    """Simulated audio capture and processing."""
+    model = st.session_state.model
+    ai_detector = st.session_state.ai_detector
+
+    while not stop_event.is_set():
+        try:
+            # Simulate real-time audio input
+            fake_audio_chunk = np.random.rand(16000 * 3).astype(np.float32)  # 3 seconds of fake audio
+            transcription = transcribe_audio(fake_audio_chunk, model)
+
+            # Update real-time transcript
+            for text in transcription:
+                st.session_state.transcript += text + "\n"
+
+                # Perform AI detection
+                ai_result = advanced_ai_detection(text, ai_detector)
+                st.session_state.ai_detection += f"Text: {text}\nResult: {ai_result}\n\n"
+
+        except Exception as e:
+            st.error(f"Error during transcription: {str(e)}")
+            break
+
+    stop_event.clear()
+
+if __name__ == "__main__":
+    run_app()