Update app.py

app.py

@@ -1,52 +1,79 @@
 import streamlit as st
-from transformers import DetrForObjectDetection, DetrImageProcessor
+from transformers import pipeline
 from PIL import Image
-import torch
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
-# Load the model and processor
-model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
-processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
-
-st.title("Context-Aware Object Detection")
-st.write("Upload an image to detect objects with contextual awareness.")
-
-# Upload an image
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-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)
-
-    # Preprocess the image and make predictions
-    inputs = processor(images=image, return_tensors="pt")
-    outputs = model(**inputs)
-
-    # Get logits and bounding boxes
-    logits = outputs.logits.softmax(-1)[0]
-    boxes = outputs.pred_boxes[0]
-
-    # Set a confidence threshold for displaying boxes
-    threshold = 0.9
-    labels = processor.tokenizer.convert_ids_to_tokens(logits.argmax(-1))
-    scores = logits.max(-1).values
-
-    # Display the image with bounding boxes
-    fig, ax = plt.subplots(1)
-    ax.imshow(image)
-
-    # Plot each detected object if it meets the confidence threshold
-    for score, label, box in zip(scores, labels, boxes):
-        if score > threshold:
-            # Convert bounding box coordinates to absolute pixel values
-            x, y, w, h = box * torch.tensor([image.width, image.height, image.width, image.height])
-            x0, y0 = x - w / 2, y - h / 2
-
-            # Draw the bounding box
-            rect = patches.Rectangle((x0, y0), w, h, linewidth=2, edgecolor='r', facecolor='none')
-            ax.add_patch(rect)
-            ax.text(x0, y0, f"{label}: {score:.2f}", color='red', fontsize=8, weight='bold')
-
-    st.pyplot(fig)
+import time
+from gtts import gTTS
+import os
+
+# Mock function to simulate image capture
+def capture_image():
+    st.write("Simulating image capture...")
+    time.sleep(1)
+    return "sample_image.jpg"  # Replace with actual path to an image file or a placeholder
+
+# Mock object detection function
+def detect_objects(image_path):
+    st.write("Detecting objects in the image...")
+    # Simulated output
+    return ["table", "chair", "lamp"]
+
+# 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
+
+# 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
+
+# Mock text-to-speech function
+def text_to_speech(text):
+    st.write("Converting summary to speech...")
+    tts = gTTS(text)
+    tts.save("summary.mp3")
+    st.audio("summary.mp3")
+
+# 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 with Hugging Face")
+    
+    # Step 1: Capture Image (simulated)
+    image_path = capture_image()
+    st.image(image_path, caption="Captured Image")
+
+    # Step 2: Detect Objects
+    detected_objects = detect_objects(image_path)
+    st.write(f"Detected Objects: {detected_objects}")
+
+    # Step 3: Filter Relevant Objects
+    setting = st.selectbox("Select Setting", ["indoor", "outdoor"], index=0)
+    relevant_objects = filter_relevant_objects(detected_objects, setting)
+    st.write(f"Relevant Objects: {relevant_objects}")
+
+    # Step 4: Generate Summary
+    summary = generate_summary(relevant_objects)
+    st.write(f"Summary: {summary}")
+
+    # Step 5: Convert Summary to Speech
+    text_to_speech(summary)
+
+    # Step 6: GPS Navigation (simulated)
+    address = st.text_input("Enter Object's Address", "1600 Amphitheatre Parkway, Mountain View, CA")
+    if st.button("Get Distance to Object"):
+        distance, duration = get_distance_to_object(address)
+        st.write(f"Distance to Object: {distance}, Duration: {duration}")
+
+if __name__ == "__main__":
+    main()