Update app.py

app.py

@@ -1,43 +1,75 @@
 import gradio as gr
 from transformers import pipeline
+from langdetect import detect
 import base64
 import io
+from pydub import AudioSegment
 
-# Load pretrained model for voice classification (AI vs Human)
-model = pipeline("audio-classification", model="microsoft/wavlm-base-sv")  # multilingual friendly
+# ----------------------------
+# Load pretrained model globally (fixes runtime error)
+# ----------------------------
+model = pipeline("audio-classification", model="microsoft/wavlm-base-sv")  # AI vs Human
 
-def classify_audio(base64_audio):
+# ----------------------------
+# Function to detect language
+# ----------------------------
+def detect_language(audio_file):
     try:
-        # Convert Base64 to bytes
-        audio_bytes = base64.b64decode(base64_audio)
-        audio_file = io.BytesIO(audio_bytes)
-        
-        # Run model
-        result = model(audio_file)[0]
-        
-        # Map labels to AI / HUMAN (example)
-        label_map = {
-            "AI_GENERATED": "AI_GENERATED",
-            "human": "HUMAN"
-        }
-        classification = label_map.get(result['label'], "HUMAN")
-        confidence = float(result['score'])
-        
-        # Return JSON
+        # Convert audio to text using Whisper (optional)
+        from transformers import pipeline as hf_pipeline
+        asr = hf_pipeline("automatic-speech-recognition", model="openai/whisper-small")
+        transcription = asr(audio_file)["text"]
+        lang = detect(transcription)
+        return lang
+    except Exception:
+        return "unknown"
+
+# ----------------------------
+# Main classification function
+# ----------------------------
+def classify_audio(input_audio):
+    try:
+        # If input is Base64 string, decode it
+        if isinstance(input_audio, str):
+            audio_bytes = base64.b64decode(input_audio)
+            audio_file = io.BytesIO(audio_bytes)
+        else:
+            audio_file = input_audio  # If it's a file from Gradio audio recorder
+
+        # Convert to WAV for model
+        audio_segment = AudioSegment.from_file(audio_file)
+        wav_io = io.BytesIO()
+        audio_segment.export(wav_io, format="wav")
+        wav_io.seek(0)
+
+        # Run AI vs Human model
+        result = model(wav_io)[0]
+        classification = "AI_GENERATED" if "AI" in result['label'].upper() else "HUMAN"
+        confidence = round(float(result['score']), 2)
+
+        # Detect language
+        wav_io.seek(0)
+        language = detect_language(wav_io)
+
+        # Return JSON for buildathon
         return {
             "classification": classification,
-            "confidence": round(confidence, 2)
+            "language": language,
+            "confidence": confidence
         }
     except Exception as e:
         return {"error": str(e)}
 
-# Gradio UI
+# ----------------------------
+# Gradio Interface
+# ----------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("### AI vs Human Voice Detector")
+    gr.Markdown("## AI vs Human Voice Detector + Language Detection")
     with gr.Row():
-        audio_input = gr.Textbox(label="Paste Base64 Audio Here")
+        audio_input = gr.Audio(source="microphone", type="filepath", label="Speak or upload audio")
         output = gr.JSON(label="Prediction")
     classify_btn = gr.Button("Classify")
     classify_btn.click(classify_audio, inputs=audio_input, outputs=output)
 
 demo.launch()
+