Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,117 +1,17 @@
 import streamlit as st
 import cv2
-import pandas as pd
-import tempfile
-import os
-from ..services.video_service import get_video_frame
-from ..services.detection_service import detect_objects
-from ..services.fault_service import detect_pole_faults
-from ..services.salesforce_dispatcher import send_to_salesforce
+import numpy as np
 
-st.title("Pole Fault Detection")
-st.write("Upload a video (.mp4) or image (.jpg, .png) to detect pole faults.")
+st.title("OpenCV Image Display Example")
 
-# File uploader
-uploaded_file = st.file_uploader("Choose a file", type=["mp4", "jpg", "png"])
+# Create a black image
+image = np.zeros((200, 200, 3), dtype=np.uint8)
 
-if uploaded_file is not None:
-    # Save uploaded file to temporary location
-    with tempfile.NamedTemporaryFile(delete=False, suffix=f".{uploaded_file.name.split('.')[-1]}") as tmp_file:
-        tmp_file.write(uploaded_file.read())
-        file_path = tmp_file.name
+# Draw a red circle
+cv2.circle(image, (100, 100), 50, (0, 0, 255), -1)
 
-    if uploaded_file.type.startswith("video"):
-        # Process video
-        st.subheader("Processed Video")
-        video_placeholder = st.empty()
-        fault_table = st.empty()
-        frame_gen = get_video_frame(file_path)
-        output_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
-        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-        cap = cv2.VideoCapture(file_path)
-        fps = cap.get(cv2.CAP_PROP_FPS)
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
-        faults_list = []
+# Convert BGR to RGB
+image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-        for frame in frame_gen:
-            # Save frame temporarily for detection
-            cv2.imwrite("temp.jpg", frame)
-            detections = detect_objects("temp.jpg")
-            faults = detect_pole_faults("temp.jpg")
-            alert_payload = {
-                "detections": detections,
-                "faults": bool(faults),
-                "fault_details": faults
-            }
-            # Send to Salesforce
-            send_to_salesforce(alert_payload)
-            faults_list.extend(faults)
-            # Annotate frame (basic text overlay for faults)
-            for fault in faults:
-                cv2.putText(
-                    frame,
-                    f"{fault['fault_type']} ({fault['confidence']:.2f})",
-                    (50, 50),
-                    cv2.FONT_HERSHEY_SIMPLEX,
-                    1,
-                    (0, 0, 255),
-                    2
-                )
-            out.write(frame)
-            # Display frame in Streamlit
-            video_placeholder.image(frame, channels="BGR")
-
-        out.release()
-        cap.release()
-        # Display final video
-        st.video(output_path)
-        # Display faults table
-        if faults_list:
-            df = pd.DataFrame(faults_list)
-            fault_table.subheader("Detected Faults")
-            fault_table.dataframe(df)
-        else:
-            fault_table.write("No faults detected.")
-        # Clean up
-        os.remove(file_path)
-        os.remove(output_path)
-        os.remove("temp.jpg")
-
-    else:
-        # Process image
-        st.subheader("Processed Image")
-        image = cv2.imread(file_path)
-        cv2.imwrite("temp.jpg", image)
-        detections = detect_objects("temp.jpg")
-        faults = detect_pole_faults("temp.jpg")
-        alert_payload = {
-            "detections": detections,
-            "faults": bool(faults),
-            "fault_details": faults
-        }
-        # Send to Salesforce
-        send_to_salesforce(alert_payload)
-        # Annotate image (basic text overlay for faults)
-        for fault in faults:
-            cv2.putText(
-                image,
-                f"{fault['fault_type']} ({fault['confidence']:.2f})",
-                (50, 50),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                1,
-                (0, 0, 255),
-                2
-            )
-        st.image(image, channels="BGR", caption="Processed Image")
-        # Display faults table
-        if faults:
-            st.subheader("Detected Faults")
-            df = pd.DataFrame(faults)
-            st.dataframe(df)
-        else:
-            st.write("No faults detected.")
-        # Clean up
-        os.remove(file_path)
-        os.remove("temp.jpg")
+# Display the image in Streamlit
+st.image(image_rgb, caption="Red Circle", use_column_width=True)