Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,69 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+
+# Load pre-trained YOLOv5 model (change to your model if custom)
+@st.cache_resource
+def load_model():
+    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # YOLOv5 small model (you can use your custom model here)
+    return model
+
+# Extract frames from the video
+def extract_frames(video_path):
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert to RGB
+        frames.append(rgb_frame)
+    cap.release()
+    return frames
+
+# Process frames with YOLOv5 model
+def process_frames_with_model(frames, model):
+    results = []
+    for frame in frames:
+        img = Image.fromarray(frame)
+        results.append(model(img))  # Use YOLOv5 to detect objects
+    return results
+
+# Display results
+def display_results(results):
+    for idx, result in enumerate(results):
+        st.subheader(f"Frame {idx+1}")
+        st.image(result.render()[0])  # Render and display frame with bounding boxes
+        st.write("Detected Faults:")
+        st.write(result.pandas().xywh)  # Display predictions as a table
+
+# Main function for Streamlit interface
+def main():
+    st.title("Solar Fault Detection - Thermal Image Analysis")
+
+    st.sidebar.header("Upload Video")
+    uploaded_video = st.sidebar.file_uploader("Choose a video file", type=["mp4", "mov"])
+
+    if uploaded_video is not None:
+        video_path = "/mnt/data/uploaded_video.mp4"
+        with open(video_path, "wb") as f:
+            f.write(uploaded_video.read())
+        st.video(uploaded_video)  # Display video preview
+
+        # Load YOLOv5 model (or your custom model)
+        model = load_model()
+
+        # Extract frames from the video
+        frames = extract_frames(video_path)
+
+        # Process frames with the YOLOv5 model
+        results = process_frames_with_model(frames, model)
+
+        # Display results on the Streamlit app
+        display_results(results)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Run the app
+if __name__ == "__main__":
+    main()