Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+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"]
+
+# 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 from Camera
+    captured_image = st.camera_input("Take a picture")
+
+    if captured_image is not None:
+        # Open the captured image
+        image = Image.open(captured_image)
+        st.image(image, caption="Captured Image", use_column_width=True)
+
+        # Step 2: Detect Objects
+        detected_objects = detect_objects(image)
+        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()