Update app.py

app.py

@@ -7,113 +7,212 @@ from tensorflow.keras.models import load_model
 from io import BytesIO
 from PIL import Image
 import requests
+# os module ki ab yahan directly zaroorat nahi hai, kyunki cache_dir specify nahi ho raha.
 
-# Authenticate and download model from Hugging Face
+# IMPORTANT: Ensure your `requirements.txt` includes tensorflow==2.10.0 or 2.15.0.
+# The `get_config` error often stems from incompatible TensorFlow versions.
+
+# 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)
+try:
+    # Download the model to a local temporary file
+    # Removing cache_dir as Hugging Face handles caching internally
+    model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    # Load the downloaded model
+    model = load_model(model_path)
+    st.success("Model loaded successfully!")
+except Exception as e:
+    st.error(f"Error loading model: {e}. Please ensure correct TensorFlow version in requirements.txt.")
+    st.stop() # Stop the app if there's an error
 
 # Set image dimensions
 img_height, img_width = 150, 150
 
 # Custom CSS
 def set_css(style):
+    """
+    Function to apply custom CSS to the webpage.
+    """
     st.markdown(f"<style>{style}</style>", unsafe_allow_html=True)
 
 combined_css = """
+    /* Main background and text colors */
     .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; }
+    /* Styling for the main content block */
+    .block-container { padding: 1rem 2rem; background-color: #333; border-radius: 10px; box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.5); margin-bottom: 20px; }
+    /* Button styling */
+    .stButton>button, .stDownloadButton>button {
+        background: linear-gradient(135deg, #ff7e5f, #feb47b); /* Colorful gradient */
+        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;
+        transition: all 0.3s ease; /* Smooth transition */
+    }
+    .stButton>button:hover, .stDownloadButton>button:hover {
+        opacity: 0.9;
+        transform: translateY(-2px); /* Lift slightly on hover */
+    }
+    /* Spinner color */
     .stSpinner { color: #4CAF50; }
+    /* Title styling */
     .title {
         font-size: 3rem;
         font-weight: bold;
         display: flex;
         align-items: center;
         justify-content: center;
+        text-shadow: 2px 2px 5px rgba(0,0,0,0.7); /* Shadow on title */
+        margin-bottom: 20px;
     }
+    /* Colorful text (gradient) */
     .colorful-text {
         background: -webkit-linear-gradient(135deg, #ff7e5f, #feb47b);
         -webkit-background-clip: text;
         -webkit-text-fill-color: transparent;
     }
+    /* Black-white text */
     .black-white-text {
-        color: black;
+        color: white; /* White color will look better */
+        margin-left: 10px; /* Give a little spacing */
     }
+    /* Input field styling */
     .small-input .stTextInput>div>input {
-        height: 2rem;
-        font-size: 0.9rem;
+        height: 2.5rem;
+        font-size: 1rem;
+        border-radius: 8px;
+        border: 1px solid #ff7e5f; /* Border matching gradient color */
+        background-color: #444;
+        color: #f0f2f6;
+        padding: 0 10px;
     }
+    /* File uploader styling */
     .small-file-uploader .stFileUploader>div>div {
-        height: 2rem;
-        font-size: 0.9rem;
+        height: 2.5rem;
+        font-size: 1rem;
     }
+    /* Custom text (subtitle) */
     .custom-text {
         font-size: 1.2rem;
         color: #feb47b;
         text-align: center;
-        margin-top: -20px;
-        margin-bottom: 20px;
+        margin-top: -10px; /* Bring closer to title */
+        margin-bottom: 30px; /* More space at bottom */
+    }
+    /* Style for expander title */
+    .stExpander {
+        border: 1px solid #feb47b;
+        border-radius: 10px;
+        background-color: #282828;
+    }
+    .stExpander div[data-baseweb="button"] {
+        color: #feb47b !important;
+        font-weight: bold;
+    }
+    .stExpander > div > div > div > div > p {
+        color: #f0f2f6 !important;
     }
 """
 
 # Streamlit application
-st.set_page_config(layout="wide")
-
-st.markdown(f"<style>{combined_css}</style>", unsafe_allow_html=True)
+st.set_page_config(layout="centered", page_title="Eye Detection") # Center layout, add a page title
+set_css(combined_css)
 
-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)
+# Title and subtitle
+st.markdown('<div class="title"><span class="colorful-text">آँख</span> <span class="black-white-text">डिटेक्शन मॉडल</span></div>', unsafe_allow_html=True)
+st.markdown('<div class="custom-text">यह पहचानने के लिए एक छवि अपलोड करें या URL प्रदान करें कि आँखें खुली हैं या बंद।</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"])
+with st.expander("इनपुट विकल्प", expanded=True):
+    url = st.text_input("छवि URL दर्ज करें", "")
+    uploaded_file = st.file_uploader("या एक छवि अपलोड करें", 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)
+    """
+    Function to load an image from a URL.
+    """
+    if not url:
+        return None
+    try:
+        response = requests.get(url, timeout=10) # Add a 10-second timeout
+        response.raise_for_status() # Raise an exception for HTTP errors
+        img = Image.open(BytesIO(response.content)).convert('RGB')
+        return np.array(img)
+    except requests.exceptions.Timeout:
+        st.error("Error: Timeout while downloading image from URL.")
+        return None
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching image from URL: {e}")
+        return None
+    except Exception as e:
+        st.error(f"Error processing image from URL: {e}")
+        return None
 
 if uploaded_file is not None or url:
+    image = None
     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)
+        image = cv2.imdecode(file_bytes, 1) # OpenCV reads in BGR format
+        st.session_state['current_image'] = image # Save image to session state
     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"
-    )
+        with st.spinner('URL से छवि लोड हो रही है...'):
+            image = load_image_from_url(url)
+        if image is not None:
+            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # PIL gives RGB, OpenCV wants BGR
+            st.session_state['current_image'] = image # Save image to session state
+        else:
+            st.warning("No valid image loaded from URL.")
+            st.session_state['current_image'] = None
+
+    if st.session_state.get('current_image') is not None:
+        image_to_predict = st.session_state['current_image']
+        # Resize and preprocess the image
+        resized_image = cv2.resize(image_to_predict, (img_height, img_width))
+        # Reshape for model input (batch_size, height, width, channels)
+        input_image = resized_image.reshape((1, img_height, img_width, 3)) / 255.0
+
+        # Perform inference
+        with st.spinner('अनुमान लगाया जा रहा है...'):
+            predictions = model.predict(input_image)
+        prediction = predictions[0][0] # Get the single scalar value
+
+        def get_label(prediction_value):
+            """
+            Get the label based on the prediction value.
+            """
+            return "खुली आँख" if prediction_value >= 0.5 else "बंद आँख"
+
+        label = get_label(prediction)
+
+        # Display the image and prediction
+        st.image(image_to_predict, channels="BGR", caption='अपलोड की गई छवि' if uploaded_file is not None else 'URL से छवि', use_column_width=True)
+        
+        # Predicted result in bold and larger font
+        st.markdown(f"### **अनुमान:** `{prediction:.4f}`")
+        st.markdown(f"### **लेबल:** <span class='{'colorful-text' if label == 'खुली आँख' else 'black-white-text'}' style='font-size: 2rem;'>{label}</span>", unsafe_allow_html=True)
+        
+        # Download button for the uploaded image (optional)
+        img_byte_arr = BytesIO()
+        # Convert from OpenCV (BGR) to PIL (RGB) so PIL can save correctly
+        img_pil = Image.fromarray(cv2.cvtColor(image_to_predict, cv2.COLOR_BGR2RGB))
+        img_pil.save(img_byte_arr, format='JPEG')
+        img_byte_arr = img_byte_arr.getvalue()
+
+        st.download_button(
+            label="छवि डाउनलोड करें",
+            data=img_byte_arr,
+            file_name="processed_eye_image.jpg",
+            mime="image/jpeg"
+        )
+    else:
+        st.info("कृपया आगे बढ़ने के लिए एक छवि अपलोड करें या URL प्रदान करें।")