Add files via upload

app.py

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+import os
+import streamlit as st
+from streamlit_option_menu import option_menu
+import numpy as np
+from PIL import Image
+import pickle
+
+from IPython.display import HTML
+import streamlit as st
+from gtts import gTTS
+from pydub import AudioSegment
+from io import BytesIO
+
+# Dictionary mapping class indices to class names
+classes = { 0:'Speed limit (20km/h)',
+            1:'Speed limit (30km/h)',
+            2:'Speed limit (50km/h)',
+            3:'Speed limit (60km/h)',
+            4:'Speed limit (70km/h)',
+            5:'Speed limit (80km/h)',
+            6:'End of speed limit (80km/h)',
+            7:'Speed limit (100km/h)',
+            8:'Speed limit (120km/h)',
+            9:'No passing',
+            10:'No passing veh over 3.5 tons',
+            11:'Right-of-way at intersection',
+            12:'Priority road',
+            13:'Yield',
+            14:'Stop',
+            15:'No vehicles',
+            16:'Vehicle > 3.5 tons prohibited',
+            17:'No entry',
+            18:'General caution',
+            19:'Dangerous curve left',
+            20:'Dangerous curve right',
+            21:'Double curve',
+            22:'Bumpy road',
+            23:'Slippery road',
+            24:'Road narrows on the right',
+            25:'Road work',
+            26:'Traffic signals',
+            27:'Pedestrians',
+            28:'Children crossing',
+            29:'Bicycles crossing',
+            30:'Beware of ice/snow',
+            31:'Wild animals crossing',
+            32:'End speed + passing limits',
+            33:'Turn right ahead',
+            34:'Turn left ahead',
+            35:'Ahead only',
+            36:'Go straight or right',
+            37:'Go straight or left',
+            38:'Keep right',
+            39:'Keep left',
+            40:'Roundabout mandatory',
+            41:'End of no passing',
+            42:'End no passing veh > 3.5 tons' }
+
+
+#D:\mine_\code files @@\pythonf\traffic_proj
+
+from keras.models import load_model
+
+# Load the model
+loaded_model = load_model('D:/mine_/code files @@/pythonf/traffic_proj/modelh.h5')
+
+
+# Load the pre-trained model
+# loaded_model = pickle.load(open('D:/mine_/code files @@/pythonf/traffic_proj/sign_model.sav', 'rb'))
+
+# import joblib
+
+# # Load the model
+# loaded_model = joblib.load('D:/mine_/code files @@/pythonf/traffic_proj/modelh.h5')
+
+
+# Function to make predictions
+def predict_traffic_sign(image):
+    # Preprocess the image
+    image = image.resize((30, 30))
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)
+    # Predict using the loaded model
+    prediction = np.argmax(loaded_model.predict(image))
+    return classes[prediction]
+
+# Function to convert text to audio and embed it in HTML
+def text_to_audio(text, language='en'):
+    # Generate audio from text
+    tts = gTTS(text=text, lang=language, slow=False)
+    # Save audio as a temporary file
+    audio_path = "prediction_audio.mp3"
+    tts.save(audio_path)
+    # Create HTML audio player with autoplay
+    audio_html = f'<audio autoplay="autoplay" controls="controls"><source src="{audio_path}" type="audio/mp3"></audio>'
+    return audio_html
+
+
+def main():
+    st.title("Traffic Sign Recognition App")
+
+    st.write("Upload an image of a traffic sign to predict its class.")
+
+    # File uploader
+    uploaded_file = st.file_uploader("upload your image here......", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Display the image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', width=400)
+
+        # Make prediction
+        prediction = predict_traffic_sign(image)
+        st.write("Prediction:", prediction)
+
+        # audio_path = "D:/mine_/code files @@/pythonf/traffic_proj/outputaudio.mp3"
+
+        # # Display the audio player
+        # st.audio(audio_path, format='audio/mp3')
+
+        # Example usage
+        # Replace this with your prediction value
+        st.write("Prediction:", prediction)
+
+        # Convert prediction text to audio and display it
+        audio_html = text_to_audio(prediction)
+        st.write(HTML(audio_html))
+
+
+
+if __name__ == '__main__':
+    main()