fixing camera n1235

src/streamlit_app.py

@@ -180,7 +180,6 @@ def load_model(confidence_threshold=0.5):
         # If no local model, load pretrained YOLOv5s
         if model is None:
             model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, trust_repo=True)
-            st.warning("⚠️ Using pretrained YOLOv5s model. For best wheelchair navigation, upload your trained model.")
         
         model.conf = confidence_threshold
         model.iou = 0.45
@@ -363,7 +362,7 @@ def main():
             st.markdown(f'<span style="color: {color}; font-weight: bold;">●</span> {class_name.title()}', unsafe_allow_html=True)
     
     # Main content tabs
-    tab1, tab2, tab3, tab4 = st.tabs(["📸 Live Detection", "📊 Analytics", "ℹ️ About", "🚀 Deployment"])
+    tab1, tab2, tab3, tab4, tab5 = st.tabs(["📸 Live Detection", "📹 Realtime Camera", "📊 Analytics", "ℹ️ About", "🚀 Deployment"])
     
     with tab1:
         col1, col2 = st.columns([2, 1])
@@ -493,6 +492,92 @@ def main():
                     st.dataframe(display_df, use_container_width=True)
     
     with tab2:
+        st.markdown("### 📹 Realtime Camera Detection")
+        st.caption("Allow camera access, then take a photo to run detection.")
+
+        camera_image = st.camera_input(
+            "Capture an image",
+            key="camera_input"
+        )
+
+        if camera_image is not None:
+            try:
+                image = Image.open(camera_image)
+            except Exception as e:
+                st.error(f"Could not read camera image: {e}")
+                image = None
+
+            if image is not None:
+                col1, col2 = st.columns(2)
+
+                with col1:
+                    st.markdown("#### 📸 Captured Image")
+                    st.image(image, width=500)
+
+                # Load model
+                if st.session_state.model is None:
+                    with st.spinner("🤖 Loading AI model..."):
+                        st.session_state.model = load_model(conf_threshold)
+
+                if st.session_state.model is not None:
+                    # Process image
+                    with st.spinner("🔍 Analyzing image for obstacles..."):
+                        start_time = time.time()
+                        rendered_img, detections, results = process_image(image, st.session_state.model, conf_threshold)
+                        processing_time = time.time() - start_time
+
+                    with col2:
+                        st.markdown("#### 🎯 Detection Results")
+                        if rendered_img is not None:
+                            st.image(rendered_img, width=500)
+
+                    # Generate navigation advice
+                    advice, safety_level = get_navigation_advice(detections, image.width)
+
+                    # Update safety status
+                    st.markdown("### 🛡️ Safety Status")
+                    if "CRITICAL" in safety_level:
+                        st.markdown(f'<div class="safety-alert"><strong>{safety_level}</strong></div>', unsafe_allow_html=True)
+                    elif "CAUTION" in safety_level:
+                        st.markdown(f'<div style="background: #fff3cd; color: #856404; padding: 1rem; border-radius: 8px; border-left: 4px solid #ffc107;"><strong>{safety_level}</strong></div>', unsafe_allow_html=True)
+                    else:
+                        st.markdown(f'<div class="success-alert"><strong>{safety_level}</strong></div>', unsafe_allow_html=True)
+
+                    # Display navigation advice
+                    st.markdown("### 🧭 Navigation Advice")
+                    for advice_text in advice:
+                        st.markdown(f'<div class="detection-box">{advice_text}</div>', unsafe_allow_html=True)
+
+                    # Performance metrics
+                    st.markdown("### ⚡ Performance Metrics")
+                    perf_col1, perf_col2, perf_col3, perf_col4 = st.columns(4)
+
+                    with perf_col1:
+                        st.metric("⏱️ Processing Time", f"{processing_time:.2f}s")
+
+                    with perf_col2:
+                        fps = 1 / processing_time if processing_time > 0 else 0
+                        st.metric("🎬 Estimated FPS", f"{fps:.1f}")
+
+                    with perf_col3:
+                        total_detections = len(detections) if detections is not None else 0
+                        st.metric("🔍 Objects Detected", total_detections)
+
+                    with perf_col4:
+                        relevant_count = len(detections) if detections is not None else 0
+                        st.metric("🎯 Relevant Objects", relevant_count)
+
+                    # Detailed detection results
+                    if detections is not None and len(detections) > 0:
+                        st.markdown("### 📋 Detailed Detection Results")
+
+                        display_df = detections[['name', 'confidence', 'xmin', 'ymin', 'xmax', 'ymax']].copy()
+                        display_df['confidence'] = display_df['confidence'].apply(lambda x: f"{x:.1%}")
+                        display_df.columns = ['Object', 'Confidence', 'X Min', 'Y Min', 'X Max', 'Y Max']
+
+                        st.dataframe(display_df, use_container_width=True)
+
+    with tab3:
         st.markdown("### 📊 Detection Analytics")
         
         if uploaded_file is not None and 'detections' in locals() and detections is not None:
@@ -527,7 +612,7 @@ def main():
         else:
             st.info("📸 Upload an image in the 'Live Detection' tab to see analytics")
     
-    with tab3:
+    with tab4:
         st.markdown("### ℹ️ About This System")
         
         col1, col2 = st.columns([2, 1])
@@ -623,7 +708,7 @@ def main():
             - Power Supply
             """)
     
-    with tab4:
+    with tab5:
         st.markdown("### 🚀 Deployment Information")
         
         col1, col2 = st.columns(2)