Delete src

src/streamlit_app.py

@@ -1,119 +0,0 @@
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-import cv2
-from huggingface_hub import hf_hub_download
-from tensorflow.keras.models import load_model
-from io import BytesIO
-from PIL import Image
-import requests
-
-# Authenticate and download model from Hugging Face
-repo_id = "Hammad712/closed_eye_detection"
-filename = "Closed_Eye_Detection_98.h5"
-model_path = hf_hub_download(repo_id=repo_id, filename=filename)
-
-# Load the downloaded model
-model = load_model(model_path)
-
-# Set image dimensions
-img_height, img_width = 150, 150
-
-# Custom CSS
-def set_css(style):
-    st.markdown(f"<style>{style}</style>", unsafe_allow_html=True)
-
-combined_css = """
-    .main, .sidebar .sidebar-content { background-color: #1c1c1c; color: #f0f2f6; }
-    .block-container { padding: 1rem 2rem; background-color: #333; border-radius: 10px; box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.5); }
-    .stButton>button, .stDownloadButton>button { background: linear-gradient(135deg, #ff7e5f, #feb47b); color: white; border: none; padding: 10px 24px; text-align: center; text-decoration: none; display: inline-block; font-size: 16px; margin: 4px 2px; cursor: pointer; border-radius: 5px; }
-    .stSpinner { color: #4CAF50; }
-    .title {
-        font-size: 3rem;
-        font-weight: bold;
-        display: flex;
-        align-items: center;
-        justify-content: center;
-    }
-    .colorful-text {
-        background: -webkit-linear-gradient(135deg, #ff7e5f, #feb47b);
-        -webkit-background-clip: text;
-        -webkit-text-fill-color: transparent;
-    }
-    .black-white-text {
-        color: black;
-    }
-    .small-input .stTextInput>div>input {
-        height: 2rem;
-        font-size: 0.9rem;
-    }
-    .small-file-uploader .stFileUploader>div>div {
-        height: 2rem;
-        font-size: 0.9rem;
-    }
-    .custom-text {
-        font-size: 1.2rem;
-        color: #feb47b;
-        text-align: center;
-        margin-top: -20px;
-        margin-bottom: 20px;
-    }
-"""
-
-# Streamlit application
-st.set_page_config(layout="wide")
-
-st.markdown(f"<style>{combined_css}</style>", unsafe_allow_html=True)
-
-st.markdown('<div class="title"><span class="colorful-text">Eye</span> <span class="black-white-text">Detection Model</span></div>', unsafe_allow_html=True)
-st.markdown('<div class="custom-text">Upload an image or provide a URL to predict whether the eyes are open or closed.</div>', unsafe_allow_html=True)
-
-# Input for image URL or path
-with st.expander("Input Options", expanded=True):
-    url = st.text_input("Enter image URL", "")
-    uploaded_file = st.file_uploader("Or upload an image", type=["jpg", "jpeg", "png"])
-
-def load_image_from_url(url):
-    response = requests.get(url)
-    img = Image.open(BytesIO(response.content)).convert('RGB')
-    return np.array(img)
-
-if uploaded_file is not None or url:
-    if uploaded_file is not None:
-        # Read the uploaded image
-        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-        image = cv2.imdecode(file_bytes, 1)
-    elif url:
-        # Read the image from URL
-        image = load_image_from_url(url)
-        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-
-    # Resize and preprocess the image
-    resized_image = cv2.resize(image, (img_height, img_width))
-    input_image = resized_image.reshape((1, img_height, img_width, 3)) / 255.0
-
-    # Perform inference
-    predictions = model.predict(input_image)
-    prediction = predictions[0][0]
-
-    def get_label(prediction):
-        return "Open Eye" if prediction >= 0.5 else "Closed Eye"
-
-    label = get_label(prediction)
-
-    # Display the image and prediction
-    st.image(image, channels="BGR", caption='Uploaded Image' if uploaded_file is not None else 'Image from URL')
-    st.markdown(f"### Prediction: {prediction:.2f}, Label: {label}")
-
-    # Provide a download button for the uploaded image (optional)
-    img_byte_arr = BytesIO()
-    img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-    img.save(img_byte_arr, format='JPEG')
-    img_byte_arr = img_byte_arr.getvalue()
-
-    st.download_button(
-        label="Download Image",
-        data=img_byte_arr,
-        file_name="processed_image.jpg",
-        mime="image/jpeg"
-    )