Update app.py

app.py

@@ -1,25 +1,66 @@
-from distutils.log import debug 
-from fileinput import filename 
-from flask import * 
-
-from source_code.utils import ML_model
-
-ml_model=ML_model()
-app = Flask(__name__)   
-  
-@app.route('/')   
-def main():   
-    return render_template("index.html")   
-  
-@app.route('/success', methods = ['POST'])   
-def success():   
-    if request.method == 'POST':   
-        f = request.files['file'] 
-        f.save(r"C:\Users\srava\OneDrive\Documents\iitj\ProjectWork\car_crash\car_crash\input_files"+f.filename)  
-        out_response=ml_model.get_prediction(r"C:\Users\srava\OneDrive\Documents\iitj\ProjectWork\car_crash\car_crash\input_files"+f.filename) 
-
-        return render_template("Acknowledgement.html", name = out_response)  
-         
-  
-if __name__ == '__main__':   
-    app.run(debug=True)
+import torch
+import librosa
+import pickle
+import numpy as np
+import gradio as gr
+
+class ML_model:
+    def __init__(self):
+        self.ml_model = torch.load("support_file/resnet_carcrash_94.pth", map_location=torch.device('cpu'))
+        self.ml_model.eval()
+        with open('support_file/indtocat.pkl', 'rb') as f:
+            self.i2c = pickle.load(f)
+
+    def spec_to_image(self, spec, eps=1e-6):
+        mean = spec.mean()
+        std = spec.std()
+        spec_norm = (spec - mean) / (std + eps)
+        spec_min, spec_max = spec_norm.min(), spec_norm.max()
+        spec_scaled = 255 * (spec_norm - spec_min) / (spec_max - spec_min)
+        spec_scaled = spec_scaled.astype(np.uint8)
+        return spec_scaled
+
+    def get_melspectrogram_db(self, file_path):
+        # Load audio file
+        wav, sr = librosa.load(file_path, sr=None)
+        sr= 44100
+        # Parameters for mel spectrogram
+        n_fft = 2048
+        hop_length = 512
+        n_mels = 128
+        fmin = 20
+        fmax = 8300
+        if wav.shape[0]<5*sr:
+            wav=np.pad(wav,int(np.ceil((5*sr-wav.shape[0])/2)),mode='reflect')
+        else:
+            wav=wav[:5*sr]
+        # Compute mel spectrogram
+        spec = librosa.feature.melspectrogram(y=wav, sr=sr, n_fft=n_fft, hop_length=hop_length, n_mels=n_mels, fmin=fmin, fmax=fmax)
+        
+        # Convert to dB scale
+        spec_db = librosa.power_to_db(spec, ref=np.max)
+        return spec_db
+    
+    def get_prediction(self, file_path):
+        spec_db = self.get_melspectrogram_db(file_path)
+        input_image = self.spec_to_image(spec_db)
+        input_tensor = torch.tensor(input_image[np.newaxis, np.newaxis, ...], dtype=torch.float32).to('cpu')
+        predictions = self.ml_model(input_tensor)
+        predicted_index = predictions.argmax(dim=1).item()
+        return self.i2c[predicted_index]
+
+def predict(file_path):
+    ml_model = ML_model()  # Initialize model
+    prediction = ml_model.get_prediction(file_path)
+    return prediction
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Audio(type="filepath", label="Upload your audio file"),
+    outputs="text",
+    title="Car Crash Sound Detection",
+    description="Upload a car crash sound clip and the model will identify the crash type.",
+    examples=["input_fileszQ1QmqrakIA_5-talking.wav","input_fileszQ1QmqrakIA_13-crash.wav"],
+)
+
+interface.launch(share=True)