Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import pandas as pd
+import tempfile
+import folium
+from reportlab.pdfgen import canvas
+from ultralytics import YOLO
+from transformers import pipeline
+import zipfile
+import os
+from streamlit_folium import st_folium
+
+# ===========================================
+# LOAD MODELS
+# ===========================================
+detection_model = YOLO("yolov5s.pt")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# ===========================================
+# GLOBAL STORAGE
+# ===========================================
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+if "incident_counter" not in st.session_state:
+    st.session_state.incident_counter = 1
+
+
+# ===========================================
+# HELPERS
+# ===========================================
+def generate_incident_id():
+    code = f"IG-2025-{st.session_state.incident_counter:05d}"
+    st.session_state.incident_counter += 1
+    return code
+
+
+def severity_badge(sev):
+    colors = {"High": "red", "Medium": "orange", "Low": "green"}
+    return f"<span style='padding:6px 12px;border-radius:6px;background:{colors[sev]};color:white;font-weight:bold'>{sev}</span>"
+
+
+def create_pdf(objects, severity, summary, lat, lon, incident_id):
+    f = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+    c = canvas.Canvas(f.name)
+    
+    c.setFont("Helvetica-Bold", 18)
+    c.drawString(100, 800, f"InfraGuard Incident Report")
+
+    c.setFont("Helvetica", 12)
+    c.drawString(100, 770, f"Incident ID: {incident_id}")
+    c.drawString(100, 750, f"Objects: {objects}")
+    c.drawString(100, 730, f"Severity: {severity}")
+    c.drawString(100, 710, f"Summary: {summary}")
+    c.drawString(100, 690, f"Location: {lat}, {lon}")
+
+    c.save()
+    return f.name
+
+
+def analyze(img, lat, lon):
+    img_np = np.array(img)
+    results = detection_model.predict(img_np, imgsz=640)
+
+    objs = [detection_model.names[int(b.cls)] for b in results[0].boxes]
+    objects_text = ", ".join(objs) if objs else "None"
+
+    # Severity
+    severity = "Low"
+    if "fire" in objs:
+        severity = "High"
+    elif any(x in objs for x in ["car", "truck", "bus"]):
+        severity = "Medium"
+
+    # AI summary
+    story = f"Detected objects: {objects_text}. Severity: {severity}."
+    try:
+        summary = summarizer(story, max_length=40, min_length=10)[0]["summary_text"]
+    except:
+        summary = story
+
+    # Annotated image
+    annotated = Image.fromarray(results[0].plot())
+
+    incident_id = generate_incident_id()
+    pdf_file = create_pdf(objects_text, severity, summary, lat, lon, incident_id)
+
+    # Save original image
+    thumb = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    img.save(thumb.name)
+
+    entry = {
+        "ID": incident_id,
+        "Image": thumb.name,
+        "Objects": objects_text,
+        "Severity": severity,
+        "Summary": summary,
+        "Latitude": lat,
+        "Longitude": lon,
+        "PDF": pdf_file
+    }
+    st.session_state.history.append(entry)
+
+    return annotated, summary, severity, pdf_file, entry
+
+
+def download_all():
+    if not st.session_state.history:
+        return None
+
+    zip_path = tempfile.NamedTemporaryFile(delete=False, suffix=".zip").name
+    with zipfile.ZipFile(zip_path, "w") as z:
+        for h in st.session_state.history:
+            z.write(h["PDF"], os.path.basename(h["PDF"]))
+    return zip_path
+
+
+# ===========================================
+# PAGE THEME / CSS
+# ===========================================
+st.markdown("""
+<style>
+body { background:#03121f !important; }
+h1 { color:#00eaff; text-align:center; }
+.reportview-container .main { background:#03121f; color:white; }
+.sidebar .sidebar-content { background:#021018; }
+</style>
+""", unsafe_allow_html=True)
+
+# ===========================================
+# UI LAYOUT
+# ===========================================
+st.title("🚨 INFRA GUARD – Cyber Security AI Dashboard")
+
+tab1, tab2 = st.tabs(["🔍 Analyze Incident", "📁 Incident History"])
+
+# -----------------------------------------------------
+# TAB 1: ANALYZE INCIDENT
+# -----------------------------------------------------
+with tab1:
+    img_file = st.file_uploader("Upload Image", type=["jpg", "png", "jpeg"])
+    lat = st.number_input("Latitude", value=24.8607)
+    lon = st.number_input("Longitude", value=67.0011)
+
+    if st.button("Analyze"):
+        if img_file is not None:
+            img = Image.open(img_file)
+
+            annotated, summary, severity, pdf_path, entry = analyze(img, lat, lon)
+
+            st.subheader("Annotated Image")
+            st.image(annotated)
+
+            st.subheader("AI Summary")
+            st.write(summary)
+
+            st.subheader("Severity")
+            st.markdown(severity_badge(severity), unsafe_allow_html=True)
+
+            st.subheader("Download PDF Report")
+            with open(pdf_path, "rb") as f:
+                st.download_button("Download PDF", f, file_name=f"{entry['ID']}.pdf")
+
+            # Map
+            m = folium.Map(location=[lat, lon], zoom_start=15)
+            folium.Marker([lat, lon]).add_to(m)
+            st_folium(m, width=700, height=350)
+
+            st.success("Incident Added to History!")
+
+# -----------------------------------------------------
+# TAB 2: HISTORY TABLE
+# -----------------------------------------------------
+with tab2:
+    if st.session_state.history:
+        df = pd.DataFrame(st.session_state.history)
+        st.dataframe(df)
+
+        del_index = st.number_input("Delete Row (Index)", min_value=0, max_value=len(df)-1, step=1)
+        if st.button("Delete Selected"):
+            st.session_state.history.pop(int(del_index))
+            st.experimental_rerun()
+
+        if st.button("Download All PDFs (ZIP)"):
+            zip_path = download_all()
+            if zip_path:
+                with open(zip_path, "rb") as f:
+                    st.download_button("Download ZIP", f, file_name="All_Reports.zip")
+    else:
+        st.info("No incidents recorded yet.")