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main.py

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+import streamlit as st
+from PIL import Image
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel, VitsModel, AutoTokenizer
+import torch
+import yolov5
+
+# Load YOLOv5 model
+@st.cache(allow_output_mutation=True)
+def load_model():
+    return yolov5.load('keremberke/yolov5m-license-plate')
+
+# Load TR-OCR model
+@st.cache(allow_output_mutation=True)
+def load_ocr_model():
+    processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+    model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
+    return processor, model
+
+# Load TTS model
+@st.cache(allow_output_mutation=True)
+def load_tts_model():
+    model = VitsModel.from_pretrained("facebook/mms-tts-eng")
+    tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
+    return model, tokenizer
+
+# Main function for Streamlit app
+def main():
+    st.title("License Plate Recognition App")
+
+    # Upload file
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    
+    # Load models on startup
+    model = load_model()
+    processor, ocr_model = load_ocr_model()
+    tts_model, tokenizer = load_tts_model()
+
+    if uploaded_file is not None:
+        img = Image.open(uploaded_file)
+        st.image(img, caption='Uploaded Image', use_column_width=True)
+
+        if st.button("Run Inference"):
+            results = model(img, size=640)
+            # results.show()
+            predictions = results.pred[0]
+            boxes = predictions[:, :4] # x1, y1, x2, y2
+            scores = predictions[:, 4]
+            categories = predictions[:, 5]
+
+            # Crop the image of the license plate
+            cropped_image = img.crop(tuple(results.xyxy[0][0, :4].squeeze().tolist()[:4]))
+            st.image(cropped_image, caption='Plate detected')
+
+            # Extract text from the image
+            pixel_values = processor(cropped_image, return_tensors="pt").pixel_values
+            generated_ids = ocr_model.generate(pixel_values)
+            generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+            st.write("Detected License Plate Text:", generated_text)
+
+            # Convert the text to audio
+            inputs = tokenizer(generated_text, return_tensors="pt")
+            with torch.no_grad():
+                output = tts_model(**inputs).waveform
+            st.audio(output.numpy(), format="audio/wav", sample_rate=tts_model.config.sampling_rate)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+Pillow==10.2.0
+streamlit==1.32.2
+torch==2.2.1
+transformers==4.40.0.dev0
+yolov5==7.0.13