Create app.py

app.py

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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.")