Update app.py

app.py

@@ -1,20 +1,53 @@
 import streamlit as st
-from transformers import pipeline
 from PIL import Image
-import time
+import cv2
+import numpy as np
 from gtts import gTTS
 import os
 
-# Mock object detection function
-def detect_objects(image):
-    st.write("Detecting objects in the image...")
-    # Simulated output
-    return ["table", "chair", "lamp"]
+# Load pre-trained model and classes
+def load_model():
+    # Load YOLO model from OpenCV
+    net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")  # Ensure these files are in the working directory
+    layer_names = net.getLayerNames()
+    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+    return net, output_layers
+
+# Object detection function
+def detect_objects(image, net, output_layers):
+    height, width, _ = image.shape
+
+    # Prepare the image for detection
+    blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+    net.setInput(blob)
+    outputs = net.forward(output_layers)
+
+    # Process the outputs
+    detected_objects = []
+    for out in outputs:
+        for detection in out:
+            scores = detection[5:]  # Get the scores for detected objects
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+
+            # Filter out weak predictions
+            if confidence > 0.5:  # Adjust threshold as needed
+                detected_objects.append(class_id)
+
+    return detected_objects
+
+# Mock function to convert class IDs to object names
+def get_object_names(class_ids):
+    # Sample mapping (extend this according to your class IDs)
+    class_names = {0: "person", 1: "bicycle", 2: "car", 3: "motorcycle", 4: "airplane",
+                   5: "bus", 6: "train", 7: "truck", 8: "boat", 9: "traffic light",
+                   10: "fire hydrant", 11: "stop sign", 12: "parking meter", 13: "bench",
+                   14: "bird", 15: "cat", 16: "dog", 17: "horse", 18: "sheep", 19: "cow"}
+    return [class_names[id] for id in class_ids if id in class_names]
 
 # Mock context-aware filter function
 def filter_relevant_objects(detected_objects, setting):
     st.write(f"Filtering relevant objects for setting: {setting}")
-    # Simulated filtering based on setting
     if setting == "indoor":
         return [obj for obj in detected_objects if obj in ["table", "lamp"]]
     return detected_objects
@@ -22,7 +55,6 @@ def filter_relevant_objects(detected_objects, setting):
 # Mock summarization function
 def generate_summary(relevant_objects):
     st.write("Generating summary for relevant objects...")
-    # Simulated summary
     summary = f"This is an {len(relevant_objects)}-item scene including: {', '.join(relevant_objects)}."
     return summary
 
@@ -36,23 +68,27 @@ def text_to_speech(text):
 # Mock GPS navigation function
 def get_distance_to_object(address):
     st.write(f"Calculating distance to address: {address}")
-    # Simulated output
     return "5 km", "15 mins"
 
 # Streamlit app main function
 def main():
-    st.title("Context-Aware Object Detection")
-    
-    # Step 1: Upload an Image
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    st.title("Context-Aware Object Detection App")
+
+    # Load the YOLO model
+    net, output_layers = load_model()
+
+    # Step 1: Capture Image from Camera
+    captured_image = st.camera_input("Take a picture")
 
-    if uploaded_file is not None:
-        # Open the uploaded image
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
+    if captured_image is not None:
+        # Open the captured image
+        image = Image.open(captured_image)
+        image_np = np.array(image)  # Convert PIL image to numpy array
+        st.image(image, caption="Captured Image", use_column_width=True)
 
         # Step 2: Detect Objects
-        detected_objects = detect_objects(image)
+        detected_ids = detect_objects(image_np, net, output_layers)
+        detected_objects = get_object_names(detected_ids)
         st.write(f"Detected Objects: {detected_objects}")
 
         # Step 3: Filter Relevant Objects