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import streamlit as st
from ultralytics import YOLO
import cv2
from gtts import gTTS
import os
import tempfile
# Load YOLOv8 model
@st.cache_resource
def load_model():
return YOLO('yolov8n.pt') # Automatically downloads YOLOv8 pre-trained model
model = load_model()
# Streamlit app title
st.title("Real-Time Object Detection for Blind Assistance")
st.write("This application detects objects in real-time from a webcam feed and provides audio feedback.")
# Start detection button
start_detection = st.button("Start Real-Time Detection")
if start_detection:
# Open webcam
st.write("Starting webcam... Press 'q' to stop.")
cap = cv2.VideoCapture(0) # 0 for default webcam, change if multiple webcams are connected
# Check if webcam is opened
if not cap.isOpened():
st.write("Error: Could not open webcam.")
else:
stframe = st.empty() # Placeholder for video frames
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Perform object detection
results = model(frame)
# Get detected object names
detected_objects = [model.names[int(box.cls)] for box in results[0].boxes]
# Generate audio feedback
if detected_objects:
objects_text = ", ".join(set(detected_objects))
summary_text = f"Detected: {objects_text}."
st.write(summary_text) # Display detected objects in Streamlit
# Convert text to speech with gTTS
tts = gTTS(text=summary_text, lang='en')
audio_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
tts.save(audio_file.name)
os.system(f"mpg123 {audio_file.name}") # Play the audio summary
# Annotate frame with bounding boxes
annotated_frame = results[0].plot()
# Display the annotated frame in Streamlit
stframe.image(annotated_frame, channels="BGR", use_column_width=True)
# Break the loop on 'q' key press
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release webcam and close OpenCV windows
cap.release()
cv2.destroyAllWindows()
st.write("Real-Time Detection Stopped.")
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