Update app.py

app.py

@@ -1,169 +1,14 @@
-import streamlit as st
-import numpy as np
-from keras.models import load_model
-import cv2
-from io import BytesIO
-import mediapipe as mp
-import tensorflow as tf
-
-# Load the model
-import os
-
-# Set environment variables
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Suppress TensorFlow warnings
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'  # Specify GPU device index
-
-# Specify GPU configuration
-config = tf.compat.v1.ConfigProto()
-config.gpu_options.allow_growth = True
-session = tf.compat.v1.Session(config=config)
-
-model_path = os.path.abspath('sign_asl_cnn_30_epochs.h5')
-if os.path.exists(model_path):
-    # Load the model
-    model = load_model(model_path)
-else:
-    print(f"File not found: {model_path}")
-class_labels = {i: str(i) if i < 10 else chr(65 + i - 10) for i in range(36)}
-
-# Function to preprocess the image
-def preprocess_image(image):
-    image = cv2.resize(image, (200, 200))
-    image = image / 255.0
-    image = image.reshape(1, 200, 200, 3)
-    return image
-
-# Function to predict the sign language letter
-def predict_letter(image):
-    processed_image = preprocess_image(image)
-    predictions = model.predict(processed_image)
-    predicted_class = np.argmax(predictions, axis=1)[0]
-    sign_letter = class_labels[predicted_class]
-    return sign_letter
-
-# Function to detect hands in the image
-def detect_hands(image):
-    mp_hands = mp.solutions.hands
-    hands = mp_hands.Hands()
-    margin = 15
-
-    # Convert the image to RGB
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-    # Process the image and get the hand landmarks
-    results = hands.process(image_rgb)
+# app.py
 
-    if results.multi_hand_landmarks:
-        for landmarks in results.multi_hand_landmarks:
-            # Get bounding box coordinates of the hand
-            landmarks_xy = [(int(landmark.x * image.shape[1]), int(landmark.y * image.shape[0]))
-                            for landmark in landmarks.landmark]
-
-            # Define the bounding box for the hand
-            x_min = max(0, min(landmarks_xy, key=lambda x: x[0])[0] - margin)
-            y_min = max(0, min(landmarks_xy, key=lambda x: x[1])[1] - margin)
-            x_max = min(image.shape[1], max(landmarks_xy, key=lambda x: x[0])[0] + margin)
-            y_max = min(image.shape[0], max(landmarks_xy, key=lambda x: x[1])[1] + margin)
-
-            # Extract the hand region
-            roi = image[y_min:y_max, x_min:x_max]
-
-            # Check if the ROI is empty
-            if roi.size == 0:
-                continue
-
-            # Resize the ROI to match your model's input shape
-            roi = cv2.resize(roi, (200, 200), interpolation=cv2.INTER_AREA)
-            hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
-
-            lower_yellow = np.array([93, 72, 51])
-            upper_yellow = np.array([224, 194, 183])
-            mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
-            roi = cv2.bitwise_and(roi, roi, mask=mask)
-            roi = roi.reshape(1, 200, 200, 3)  # Ensure it matches your model's input shape
-
-            # Make predictions using your classifier
-            predictions = model.predict(roi)
-            predicted_class = int(np.argmax(predictions, axis=1)[0])
-            result = class_labels[predicted_class]
-
-            # Draw result on the image
-            cv2.putText(image, str(result), (x_min, y_min - 10),
-                        cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
-
-            # Draw bounding box on the image
-            cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (255, 0, 0), 2)
-
-    return image
+import streamlit as st
+import requests
 
-# Streamlit app
 st.title('Sign Language Recognition')
 
-# Sidebar with radio button for Upload/Webcam
-selected_option = st.sidebar.radio("Select Option", ["Upload", "Webcam"], index=0)
-
-if selected_option == "Upload":
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"])
-
-    if uploaded_file is not None:
-        if st.button('Predict'):
-            contents = uploaded_file.read()
-            nparr = np.frombuffer(contents, np.uint8)
-            image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-            # Make the prediction
-            predicted_letter = predict_letter(image)
-
-            # Display the predicted letter
-            st.write('Predicted Letter:', predicted_letter)
-
-elif selected_option == "Webcam":
-    # Placeholder for webcam frame
-    webcam_frame = st.empty()
-
-    # Placeholder for predicted letter in webcam mode
-    predicted_letter_webcam = st.empty()
-
-    # Placeholder for webcam capture status
-    webcam_capture_status = st.empty()
-
-    # Placeholder for webcam stop button
-    webcam_stop_button = st.empty()
-
-    # Placeholder for webcam status
-    webcam_status = st.empty()
-
-    # Placeholder for webcam button
-    webcam_button = st.button("Start Webcam")
-
-    if webcam_button:
-        webcam_status.text("Webcam is on.")
-        webcam_stop_button = st.button("Stop Webcam")
-
-        # OpenCV video capture
-        cap = cv2.VideoCapture(0)
-
-        while True:
-            # Read the frame from the webcam
-            ret, frame = cap.read()
-
-            # Display the frame in Streamlit
-            webcam_frame.image(frame, channels="BGR")
-
-            # Detect hands in the current frame
-            frame = detect_hands(frame)
-
-            # Convert the frame to JPEG format
-            _, jpeg = cv2.imencode(".jpg", frame)
-
-            # Display the predicted letter
-            predicted_letter = predict_letter(frame)
-            predicted_letter_webcam.text(f"Predicted Letter: {predicted_letter}")
-
-            # Check if the "Stop Webcam" button is clicked
-            if webcam_stop_button:
-                webcam_status.text("Webcam is off.")
-                break
-
-        # Release the webcam when done
-        cap.release()
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"])
+if uploaded_file is not None:
+    if st.button('Predict'):
+        files = {'file': uploaded_file.getvalue()}
+        response = requests.post('http://74.12.105.219:8090/predict', files=files)
+        result = response.json()
+        st.write('Predicted Letter: ', result['predicted_letter'])