Delete app.py

app.py

@@ -1,115 +0,0 @@
-import streamlit as st
-import cv2
-import numpy as np
-import tempfile
-import os
-import pandas as pd
-from PIL import Image
-import torch
-from transformers import DetrImageProcessor, DetrForObjectDetection
-
-# === Hugging Face Model Configuration ===
-processor = DetrImageProcessor.from_pretrained("NaveenKumar5/Solar_panel_fault_detection")
-model = DetrForObjectDetection.from_pretrained("NaveenKumar5/Solar_panel_fault_detection")
-model.eval()
-
-# === Streamlit App Configuration ===
-st.set_page_config(page_title="Solar Panel Fault Detection", layout="wide")
-st.title("🔍 Solar Panel Fault Detection (DETR - Hugging Face)")
-st.write("Upload a thermal video (MP4). Faults will be detected using your Hugging Face DETR model.")
-
-# === Fault Detection Function ===
-def detect_faults(frame, frame_idx, fps):
-    image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-    inputs = processor(images=image, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    target_sizes = torch.tensor([image.size[::-1]])  # (height, width)
-    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]
-
-    faults = []
-    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        x1, y1, x2, y2 = map(int, box.tolist())
-        conf = score.item()
-        label_id = label.item()
-        label_name = model.config.id2label[label_id]  # Use proper label from config if available
-
-        # Draw on frame
-        color = (0, 0, 255)
-        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
-        cv2.putText(frame, f"{label_name} ({conf:.2f})", (x1, y1 - 5),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-        faults.append({
-            "Frame": frame_idx,
-            "Time (s)": round(frame_idx / fps, 2),
-            "Fault Type": label_name,
-            "Confidence": round(conf, 2),
-            "Box": f"({x1},{y1},{x2},{y2})"
-        })
-    return frame, faults
-
-# === Video Processing Function ===
-def process_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
-    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
-    output_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
-    writer = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
-
-    fault_log = []
-    progress = st.progress(0)
-
-    for frame_idx in range(total_frames):
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        # Detect every second
-        if frame_idx % fps == 0:
-            frame, faults = detect_faults(frame, frame_idx, fps)
-            fault_log.extend(faults)
-
-        writer.write(frame)
-        progress.progress(min(frame_idx / total_frames, 1.0))
-
-    cap.release()
-    writer.release()
-    return output_path, fault_log
-
-# === CSV Conversion ===
-def convert_df(df):
-    return df.to_csv(index=False).encode('utf-8')
-
-# === Streamlit UI ===
-uploaded_file = st.file_uploader("📤 Upload thermal video", type=["mp4"])
-if uploaded_file:
-    st.video(uploaded_file)
-
-    temp_input_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
-    with open(temp_input_path, "wb") as f:
-        f.write(uploaded_file.read())
-
-    output_path, log = process_video(temp_input_path)
-
-    st.subheader("🧪 Processed Output")
-    st.video(output_path)
-
-    if log:
-        df = pd.DataFrame(log)
-        st.write("### 📊 Detected Faults Table")
-        st.dataframe(df)
-        st.download_button("📥 Download Fault Log CSV", convert_df(df), "fault_log.csv", "text/csv")
-    else:
-        st.success("✅ No faults detected.")
-
-    os.unlink(temp_input_path)
-    os.unlink(output_path)
-
-st.markdown("---")
-st.caption("Built with Streamlit + Hugging Face DETR + OpenCV")