Update app.py

app.py

@@ -1,128 +1,114 @@
 import streamlit as st
-import cv2
+import pickle
 import numpy as np
-import easyocr
-from ultralytics import YOLO
-
-# Title of the app
-st.title("License Plate Recognition System🚗")
-
-# Load the YOLO model for license plate detection
-@st.cache_resource
-def load_yolo_model():
-    model_path = "best.pt"  # Replace with your model file
-    model = YOLO(model_path)
-    return model
-
-# Load EasyOCR reader
-@st.cache_resource
-def load_easyocr_reader():
-    return easyocr.Reader(['en'], gpu=False)
-
-# Initialize models
-yolo_model = load_yolo_model()
-ocr_reader = load_easyocr_reader()
-
-# Function to process image and detect license plates
-def process_image(image, confidence_threshold=0.5):
-    # Perform license plate detection
-    results = yolo_model(image, conf=confidence_threshold)
-    annotated_image = cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)
-    st.image(annotated_image, caption="Detected License Plate(s)", use_container_width=True)
-    
-    # Loop through detections and perform OCR
-    for result in results:
-        boxes = result.boxes.xyxy.cpu().numpy().astype(int)
-        if len(boxes) == 0:
-            st.warning("No license plate detected!")
-            return
-        for i, box in enumerate(boxes):
-            x1, y1, x2, y2 = box
-            cropped_image = image[y1:y2, x1:x2]
-            cropped_image_rgb = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)
-            st.image(cropped_image_rgb, caption=f"Cropped License Plate {i+1}", use_container_width=True)
-            
-            # Perform OCR on the cropped image
-            text_results = ocr_reader.readtext(cropped_image_rgb, detail=0)
-            detected_text = " ".join(text_results)
-            st.write(f"**Extracted Text (Plate {i+1}):** {detected_text}")
-            st.write(f"**Confidence Score:** {result.boxes.conf.cpu().numpy()[i]:.2f}")
-
-# Function to process video and detect license plates
-def process_video(video_path, confidence_threshold=0.5, output_path="output_video.mp4"):
-    # Open the video file
-    cap = cv2.VideoCapture(video_path)
-
-    # Get video frame dimensions
-    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
-    # Create VideoWriter object to save the output video
-    fourcc = cv2.VideoWriter_fourcc(*"mp4v")  # Codec for mp4
-    out = cv2.VideoWriter(output_path, fourcc, 20.0, (frame_width, frame_height))  # 20 FPS
-
-    if not cap.isOpened():
-        st.error("Error opening video stream or file")
-        return
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        results = yolo_model(frame, conf=confidence_threshold)
-        annotated_frame = cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)
-
-        # Loop through detections and perform OCR
-        for result in results:
-            boxes = result.boxes.xyxy.cpu().numpy().astype(int)
-            for i, box in enumerate(boxes):
-                x1, y1, x2, y2 = box
-                cropped_plate = frame[y1:y2, x1:x2]
-                cropped_rgb = cv2.cvtColor(cropped_plate, cv2.COLOR_BGR2RGB)
-
-                # Perform OCR on the cropped image
-                text_results = ocr_reader.readtext(cropped_rgb, detail=0)
-                detected_text = " ".join(text_results)
-
-                # Optionally add detected text on the annotated frame
-                cv2.putText(annotated_frame, detected_text, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
-
-        # Write the annotated frame to the output video
-        out.write(cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR))
-
-    cap.release()
-    out.release()
-
-    st.success(f"Video processing complete. Output video saved to {output_path}")
-
-    # Provide a download link for the processed video
-    with open(output_path, "rb") as f:
-        st.download_button(label="Download Processed Video", data=f, file_name=output_path)
-
-# Sidebar input for file upload
-uploaded_file = st.file_uploader("Upload an Image or Video", type=["mp4", "avi", "mov", "jpg", "jpeg", "png"])
+import cv2
+from PIL import Image
+
+# Load the trained model and other necessary files
+model = pickle.load(open('artifacts/license_plate_model.pkl', 'rb'))
+
+# Function to load custom CSS
+def local_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+# Apply custom CSS for styling
+local_css("style.css")
+
+# Page configuration
+st.set_page_config(page_title='License Plate Detection', layout='centered')
+
+# Add a fancy header with emojis
+st.markdown("""
+    <div class="glass">
+        <h1>🚗 License Plate Detection 🚗</h1>
+        <p>Upload an image to detect license plates</p>
+    </div>
+""", unsafe_allow_html=True)
+
+# File uploader for image input
+st.markdown("""
+    <div class="glass">
+        <p>📸 Upload a car image for license plate detection</p>
+    </div>
+""", unsafe_allow_html=True)
+
+uploaded_file = st.file_uploader("Choose a file", type=["jpg", "jpeg", "png"])
 
+# If image is uploaded, process the image and make predictions
 if uploaded_file is not None:
-    # Check if it's an image or video
-    file_type = uploaded_file.type
-
-    if file_type.startswith("image"):
-        # Read and process the image
-        image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1)
-        confidence_threshold = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.5, 0.01)
-        process_image(image, confidence_threshold)
-
-    elif file_type.startswith("video"):
-        # Save the uploaded video to a temporary file
-        video_bytes = uploaded_file.read()
-        video_path = "/tmp/uploaded_video.mp4"
-        with open(video_path, "wb") as f:
-            f.write(video_bytes)
-
-        # Process the video and save the output
-        output_path = "/tmp/output_video.mp4"
-        process_video(video_path, confidence_threshold=0.5, output_path=output_path)
-
-st.markdown("---")
-st.info("This application uses FIne Tuned YOLOv8 for detection and EasyOCR for text recognition.")
+    # Load the image using PIL
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Convert image to OpenCV format (for model processing)
+    img_array = np.array(image)
+    img_gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+
+    # Use the model to detect license plate (model predictions are made here)
+    # Assuming your model is a classifier or detector for the license plate
+    plate_detected = model.predict(img_gray)  # Modify this depending on your model
+
+    if plate_detected:
+        st.markdown("""
+            <div class="glass">
+                <p>✔️ License Plate Detected!</p>
+            </div>
+        """, unsafe_allow_html=True)
+    else:
+        st.markdown("""
+            <div class="glass">
+                <p>❌ No License Plate Detected!</p>
+            </div>
+        """, unsafe_allow_html=True)
+
+# Apply custom button styling to Streamlit's default button using CSS
+st.markdown("""
+    <style>
+        .stButton > button {
+            background: linear-gradient(135deg, #8B5E3C, #B8860B); /* Warm vintage gold-brown gradient */
+            border: none;
+            color: white; /* White text */
+            padding: 12px 24px;
+            text-align: center;
+            text-decoration: none;
+            display: inline-block;
+            font-size: 16px;
+            margin: 10px 5px;
+            cursor: pointer;
+            border-radius: 12px; /* Rounded corners for a soft vintage touch */
+            box-shadow: 0 6px 12px rgba(0, 0, 0, 0.25); /* Soft, deep shadow for antique feel */
+            font-family: 'Georgia', serif; /* Classic serif font for the button */
+            text-transform: uppercase; /* Uppercase text for a formal touch */
+        }
+
+        .stButton > button:hover {
+            background: linear-gradient(135deg, #704214, #B8860B); /* Slightly darker gold-brown */
+            box-shadow: 0 8px 16px rgba(0, 0, 0, 0.3); /* Stronger shadow for depth on hover */
+            transform: translateY(-2px); /* Lift effect */
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Optionally, add more interactivity or styling to the button
+if st.button('Detect License Plate'):
+    # Trigger the license plate detection when the button is pressed
+    pass  # Include any additional logic here, if necessary
+
+# Glassmorphism styling for text elements
+st.markdown("""
+    <style>
+        h1, h2, h3, h4 {
+            font-family: 'Georgia', serif;
+            color: #ffffff;
+            text-align: center;
+            text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.5);
+        }
+
+        p {
+            font-family: 'Georgia', serif;
+            color: #ffffff;
+            text-align: center;
+        }
+    </style>
+""", unsafe_allow_html=True)