import streamlit as st
import requests
from ultralytics import YOLO
import numpy as np
from PIL import Image
import io
import base64
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import json
import os
from datetime import datetime
from fpdf import FPDF
# --- 1. Global Backend Check ---
API_BASE_URL = "http://localhost:8000"
def check_backend():
try:
res = requests.get(f"{API_BASE_URL}/get_confidence", timeout=2)
return res.status_code == 200
except:
return False
backend_active = check_backend()
# LOCAL MODEL LOADING REMOVED (YOLO26 Clean Sweep)
# UI now relies entirely on Backend API for NMS-Free inference.
if not backend_active:
st.error("⚠️ Backend API is offline!")
st.info("Please start the backend server first (e.g., `python main.py`) to unlock AI features.")
if st.button("🔄 Retry Connection"):
st.rerun()
st.stop() # Stops execution here, effectively disabling the app
# --- 2. Main Page Config (Only rendered if backend is active) ---
st.set_page_config(page_title="Palm Oil Ripeness AI (YOLO26)", layout="wide")
st.title("🌴 Palm Oil FFB Management System")
st.markdown("### Production-Ready AI Analysis & Archival")
# --- Sidebar ---
st.sidebar.header("Backend Controls")
def update_confidence():
new_conf = st.session_state.conf_slider
try:
requests.post(f"{API_BASE_URL}/set_confidence", json={"threshold": new_conf})
st.toast(f"Threshold updated to {new_conf}")
except:
st.sidebar.error("Failed to update threshold")
# We already know backend is up here
response = requests.get(f"{API_BASE_URL}/get_confidence")
current_conf = response.json().get("current_confidence", 0.25)
st.sidebar.success(f"Connected to API")
st.sidebar.info("Engine: YOLO26 NMS-Free (Inference: ~39ms)")
# Synchronized Slider
st.sidebar.slider(
"Confidence Threshold",
0.1, 1.0,
value=float(current_conf),
key="conf_slider",
on_change=update_confidence
)
st.sidebar.markdown("---")
st.sidebar.subheader("Inference Engine")
engine_choice = st.sidebar.selectbox(
"Select Model Engine",
["YOLO26 (ONNX - High Speed)", "YOLO26 (PyTorch - Native)"],
index=0,
help="ONNX is optimized for latency. PyTorch provides native object handling."
)
model_type = "onnx" if "ONNX" in engine_choice else "pytorch"
if model_type == "pytorch":
st.sidebar.warning("PyTorch Engine: Higher Memory Usage")
else:
st.sidebar.info("ONNX Engine: ~39ms Latency")
# Helper to reset results when files change
def reset_single_results():
st.session_state.last_detection = None
def reset_batch_results():
st.session_state.last_batch_results = None
# MPOB Color Map for Overlays (Global for consistency)
overlay_colors = {
'Ripe': '#22c55e', # Industrial Green
'Underripe': '#fbbf24', # Industrial Orange
'Unripe': '#3b82f6', # Industrial Blue
'Abnormal': '#dc2626', # Critical Red
'Empty_Bunch': '#64748b',# Waste Gray
'Overripe': '#7c2d12' # Dark Brown/Orange
}
def display_interactive_results(image, detections, key=None):
"""Renders image with interactive hover-boxes using Plotly."""
img_width, img_height = image.size
fig = go.Figure()
# Add the palm image as the background
fig.add_layout_image(
dict(source=image, x=0, y=img_height, sizex=img_width, sizey=img_height,
sizing="stretch", opacity=1, layer="below", xref="x", yref="y")
)
# Configure axes to match image dimensions
fig.update_xaxes(showgrid=False, range=(0, img_width), zeroline=False, visible=False)
fig.update_yaxes(showgrid=False, range=(0, img_height), zeroline=False, visible=False, scaleanchor="x")
# Add interactive boxes
for i, det in enumerate(detections):
x1, y1, x2, y2 = det['box']
# Plotly y-axis is inverted relative to PIL, so we flip y
y_top, y_bottom = img_height - y1, img_height - y2
color = overlay_colors.get(det['class'], "#ffeb3b")
# The 'Hover' shape
bunch_id = det.get('bunch_id', i+1)
fig.add_trace(go.Scatter(
x=[x1, x2, x2, x1, x1],
y=[y_top, y_top, y_bottom, y_bottom, y_top],
fill="toself",
fillcolor=color,
opacity=0.3, # Semi-transparent until hover
mode='lines',
line=dict(color=color, width=3),
name=f"Bunch #{bunch_id}",
text=f"ID: #{bunch_id}
Grade: {det['class']}
Score: {det['confidence']:.2f}
Alert: {det['is_health_alert']}",
hoverinfo="text"
))
fig.update_layout(width=800, height=600, margin=dict(l=0, r=0, b=0, t=0), showlegend=False)
st.plotly_chart(fig, use_container_width=True, key=key)
def annotate_image(image, detections):
"""Draws high-visibility boxes and background-shaded labels."""
from PIL import ImageDraw, ImageFont
draw = ImageDraw.Draw(image)
# Dynamic font size based on image resolution
font_size = max(20, image.width // 40)
try:
font_path = "C:\\Windows\\Fonts\\arial.ttf"
if os.path.exists(font_path):
font = ImageFont.truetype(font_path, font_size)
else:
font = ImageFont.load_default()
except:
font = ImageFont.load_default()
for det in detections:
box = det['box'] # [x1, y1, x2, y2]
cls = det['class']
conf = det['confidence']
bunch_id = det.get('bunch_id', '?')
color = overlay_colors.get(cls, '#ffffff')
# 1. Draw Bold Bounding Box
draw.rectangle(box, outline=color, width=max(4, image.width // 200))
# 2. Draw Label Background (High Contrast)
label = f"#{bunch_id} {cls} {conf:.2f}"
try:
# textbbox provides precise coordinates for background rectangle
l, t, r, b = draw.textbbox((box[0], box[1] - font_size - 10), label, font=font)
draw.rectangle([l-5, t-5, r+5, b+5], fill=color)
draw.text((l, t), label, fill="white", font=font)
except:
# Fallback for basic text drawing
draw.text((box[0], box[1] - 25), label, fill=color)
return image
def generate_batch_report(data, uploaded_files_map=None):
"""Generates a professional PDF report for batch results with visual evidence."""
from PIL import ImageDraw
pdf = FPDF()
pdf.add_page()
pdf.set_font("Arial", "B", 16)
pdf.cell(190, 10, "Palm Oil FFB Harvest Quality Report", ln=True, align="C")
pdf.set_font("Arial", "", 12)
pdf.cell(190, 10, f"Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True, align="C")
pdf.ln(10)
# 1. Summary Table
pdf.set_font("Arial", "B", 14)
pdf.cell(190, 10, "1. Batch Summary", ln=True)
pdf.set_font("Arial", "", 12)
summary = data.get('industrial_summary', {})
total_bunches = data.get('total_count', 0)
pdf.cell(95, 10, "Metric", border=1)
pdf.cell(95, 10, "Value", border=1, ln=True)
pdf.cell(95, 10, "Total Bunches Detected", border=1)
pdf.cell(95, 10, str(total_bunches), border=1, ln=True)
for grade, count in summary.items():
if count > 0:
pdf.cell(95, 10, f"Grade: {grade}", border=1)
pdf.cell(95, 10, str(count), border=1, ln=True)
pdf.ln(10)
# 2. Strategic Insights
pdf.set_font("Arial", "B", 14)
pdf.cell(190, 10, "2. Strategic Yield Insights", ln=True)
pdf.set_font("Arial", "", 12)
unripe = summary.get('Unripe', 0)
underripe = summary.get('Underripe', 0)
loss = unripe + underripe
if loss > 0:
pdf.multi_cell(190, 10, f"WARNING: {loss} bunches were harvested before peak ripeness. "
"This directly impacts the Oil Extraction Rate (OER) and results in potential yield loss.")
else:
pdf.multi_cell(190, 10, "EXCELLENT: All detected bunches meet prime ripeness standards. Harvest efficiency is 100%.")
# Critical Alerts
abnormal = summary.get('Abnormal', 0)
empty = summary.get('Empty_Bunch', 0)
if abnormal > 0 or empty > 0:
pdf.ln(5)
pdf.set_text_color(220, 0, 0)
pdf.set_font("Arial", "B", 12)
pdf.cell(190, 10, "CRITICAL HEALTH ALERTS:", ln=True)
pdf.set_font("Arial", "", 12)
if abnormal > 0:
pdf.cell(190, 10, f"- {abnormal} Abnormal Bunches detected (Requires immediate field inspection).", ln=True)
if empty > 0:
pdf.cell(190, 10, f"- {empty} Empty Bunches detected (Waste reduction needed).", ln=True)
pdf.set_text_color(0, 0, 0)
# 3. Visual Evidence Section
if 'detailed_results' in data and uploaded_files_map:
pdf.add_page()
pdf.set_font("Arial", "B", 14)
pdf.cell(190, 10, "3. Visual Batch Evidence (AI Overlay)", ln=True)
pdf.ln(5)
# Group detections by filename
results_by_file = {}
for res in data['detailed_results']:
fname = res['filename']
if fname not in results_by_file:
results_by_file[fname] = []
results_by_file[fname].append(res['detection'])
for fname, detections in results_by_file.items():
if fname in uploaded_files_map:
img_bytes = uploaded_files_map[fname]
img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
draw = ImageDraw.Draw(img)
# Drawing annotated boxes for PDF using high-visibility utility
annotate_image(img, detections)
# Save to temp file for PDF
temp_img_path = f"temp_report_{fname}"
img.save(temp_img_path)
# Check if we need a new page based on image height (rough estimate)
if pdf.get_y() > 200:
pdf.add_page()
pdf.image(temp_img_path, x=10, w=150)
pdf.set_font("Arial", "I", 10)
pdf.cell(190, 10, f"Annotated: {fname}", ln=True)
pdf.ln(5)
os.remove(temp_img_path)
# Footer
pdf.set_y(-15)
pdf.set_font("Arial", "I", 8)
pdf.cell(190, 10, "Generated by Palm Oil AI Desktop PoC - YOLO26 Engine", align="C")
return pdf.output(dest='S')
# --- Tabs ---
tab1, tab2, tab3, tab4 = st.tabs(["Single Analysis", "Batch Processing", "Similarity Search", "History Vault"])
# --- Tab 1: Single Analysis ---
with tab1:
st.subheader("Analyze Single Bunch")
uploaded_file = st.file_uploader(
"Upload a bunch image...",
type=["jpg", "jpeg", "png"],
key="single",
on_change=reset_single_results
)
if uploaded_file:
# State initialization
if "last_detection" not in st.session_state:
st.session_state.last_detection = None
# 1. Auto-Detection Trigger
if uploaded_file and st.session_state.last_detection is None:
with st.spinner(f"Processing with {model_type.upper()} Engine..."):
files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)}
payload = {"model_type": model_type}
res = requests.post(f"{API_BASE_URL}/analyze", files=files, data=payload)
if res.status_code == 200:
st.session_state.last_detection = res.json()
st.rerun() # Refresh to show results immediately
else:
st.error(f"Detection Failed: {res.text}")
# 2. Results Layout
if st.session_state.last_detection:
st.divider()
# PRIMARY ANNOTATED VIEW
st.write("### 🔍 AI Analytical View")
data = st.session_state.last_detection
img = Image.open(uploaded_file).convert("RGB")
display_interactive_results(img, data['detections'], key="main_viewer")
# Visual Legend
st.write("#### 🎨 Ripeness Legend")
l_cols = st.columns(len(overlay_colors))
for i, (grade, color) in enumerate(overlay_colors.items()):
with l_cols[i]:
st.markdown(f'{grade}
', unsafe_allow_html=True)
st.divider()
st.write("### 📈 Manager's Dashboard")
m_col1, m_col2, m_col3, m_col4 = st.columns(4)
with m_col1:
st.metric("Total Bunches", data.get('total_count', 0))
with m_col2:
st.metric("Healthy (Ripe)", data['industrial_summary'].get('Ripe', 0))
with m_col3:
abnormal = data['industrial_summary'].get('Abnormal', 0)
st.metric("Abnormal Alerts", abnormal, delta=-abnormal, delta_color="inverse")
with m_col4:
st.metric("Inference Speed", f"{data.get('inference_ms', 0):.1f} ms")
col1, col2 = st.columns([1.5, 1]) # Keep original col structure for summary below
with col1:
with st.expander("🛠️ Technical Evidence: Raw Output Tensor", expanded=False):
st.write("First 5 detections from raw output tensor:")
st.json(data.get('raw_array_sample', []))
with st.container(border=True):
st.write("### 🏷️ Detection Results")
if not data['detections']:
st.warning("No Fresh Fruit Bunches detected.")
else:
for det in data['detections']:
st.info(f"### Bunch #{det['bunch_id']}: {det['class']} ({det['confidence']:.2%})")
st.write("### 📊 Harvest Quality Mix")
# Convert industrial_summary dictionary to a DataFrame for charting
summary_df = pd.DataFrame(
list(data['industrial_summary'].items()),
columns=['Grade', 'Count']
)
# Filter out classes with 0 count for a cleaner chart
summary_df = summary_df[summary_df['Count'] > 0]
if not summary_df.empty:
# Create a Pie Chart to show the proportion of each grade
fig = px.pie(summary_df, values='Count', names='Grade',
color='Grade',
color_discrete_map={
'Ripe': '#22c55e', # Industrial Green
'Underripe': '#fbbf24', # Industrial Orange
'Unripe': '#3b82f6', # Industrial Blue
'Abnormal': '#dc2626', # Critical Red
'Empty_Bunch': '#64748b' # Waste Gray
},
hole=0.4)
fig.update_layout(margin=dict(t=0, b=0, l=0, r=0), height=300)
st.plotly_chart(fig, width='stretch', key="single_pie")
# 💡 Strategic R&D Insight: Harvest Efficiency
st.write("---")
st.write("#### 💡 Strategic R&D Insight")
unripe_count = data['industrial_summary'].get('Unripe', 0)
underripe_count = data['industrial_summary'].get('Underripe', 0)
total_non_prime = unripe_count + underripe_count
st.write(f"🌑 **Unripe (Mentah):** {unripe_count}")
st.write(f"🌗 **Underripe (Kurang Masak):** {underripe_count}")
if total_non_prime > 0:
st.warning(f"🚨 **Potential Yield Loss:** {total_non_prime} bunches harvested too early. This will reduce OER (Oil Extraction Rate).")
else:
st.success("✅ **Harvest Efficiency:** 100% Prime Ripeness detected.")
# High-Priority Health Alert
if data['industrial_summary'].get('Abnormal', 0) > 0:
st.error(f"🚨 CRITICAL: {data['industrial_summary']['Abnormal']} Abnormal Bunches Detected!")
if data['industrial_summary'].get('Empty_Bunch', 0) > 0:
st.warning(f"⚠️ ALERT: {data['industrial_summary']['Empty_Bunch']} Empty Bunches Detected.")
# 3. Cloud Actions (Only if detections found)
st.write("---")
st.write("#### ✨ Cloud Archive")
if st.button("🚀 Save to Atlas (Vectorize)", width='stretch'):
with st.spinner("Archiving..."):
import json
primary_det = data['detections'][0]
payload = {"detection_data": json.dumps(primary_det)}
files_cloud = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)}
res_cloud = requests.post(f"{API_BASE_URL}/vectorize_and_store", files=files_cloud, data=payload)
if res_cloud.status_code == 200:
res_json = res_cloud.json()
if res_json["status"] == "success":
st.success(f"Archived! ID: `{res_json['record_id'][:8]}...`")
else:
st.error(f"Cloud Error: {res_json['message']}")
else:
st.error("Failed to connect to cloud service")
if st.button("🚩 Flag Misclassification", width='stretch', type="secondary"):
# Save to local feedback folder
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
feedback_id = f"fb_{timestamp}"
img_path = f"feedback/{feedback_id}.jpg"
json_path = f"feedback/{feedback_id}.json"
# Save image
Image.open(uploaded_file).save(img_path)
# Save metadata
feedback_data = {
"original_filename": uploaded_file.name,
"timestamp": timestamp,
"detections": data['detections'],
"threshold_used": data['current_threshold']
}
with open(json_path, "w") as f:
json.dump(feedback_data, f, indent=4)
st.toast("✅ Feedback saved to local vault!", icon="🚩")
if st.button("💾 Local History Vault (Auto-Saved)", width='stretch', type="secondary", disabled=True):
pass
st.caption("✅ This analysis was automatically archived to the local vault.")
# --- Tab 2: Batch Processing ---
with tab2:
st.subheader("Bulk Analysis")
# 1. Initialize Session State
if "batch_uploader_key" not in st.session_state:
st.session_state.batch_uploader_key = 0
if "last_batch_results" not in st.session_state:
st.session_state.last_batch_results = None
# 2. Display Persisted Results (if any)
if st.session_state.last_batch_results:
res_data = st.session_state.last_batch_results
with st.container(border=True):
st.success(f"✅ Successfully processed {res_data['processed_count']} images.")
# Batch Summary Dashboard
st.write("### 📈 Batch Quality Overview")
batch_summary = res_data.get('industrial_summary', {})
if batch_summary:
sum_df = pd.DataFrame(list(batch_summary.items()), columns=['Grade', 'Count'])
sum_df = sum_df[sum_df['Count'] > 0]
b_col1, b_col2 = st.columns([1, 1])
with b_col1:
st.dataframe(sum_df, hide_index=True, width='stretch')
with b_col2:
if not sum_df.empty:
fig_batch = px.bar(sum_df, x='Grade', y='Count', color='Grade',
color_discrete_map={
'Ripe': '#22c55e',
'Underripe': '#fbbf24',
'Unripe': '#3b82f6',
'Abnormal': '#dc2626',
'Empty_Bunch': '#64748b'
})
fig_batch.update_layout(margin=dict(t=0, b=0, l=0, r=0), height=200, showlegend=False)
st.plotly_chart(fig_batch, width='stretch', key="batch_bar")
if batch_summary.get('Abnormal', 0) > 0:
st.error(f"🚨 BATCH CRITICAL: {batch_summary['Abnormal']} Abnormal Bunches found in this batch!")
st.write("Generated Record IDs:")
st.code(res_data['record_ids'])
# --- 4. Batch Evidence Gallery ---
st.write("### 🖼️ Detailed Detection Evidence")
if 'detailed_results' in res_data:
# Group results by filename for gallery
gallery_map = {}
for res in res_data['detailed_results']:
fname = res['filename']
if fname not in gallery_map:
gallery_map[fname] = []
gallery_map[fname].append(res['detection'])
# Show images with overlays using consistent utility
for up_file in uploaded_files:
if up_file.name in gallery_map:
with st.container(border=True):
g_img = Image.open(up_file).convert("RGB")
g_annotated = annotate_image(g_img, gallery_map[up_file.name])
st.image(g_annotated, caption=f"Evidence: {up_file.name}", use_container_width=True)
# PDF Export Button (Pass images map)
files_map = {f.name: f.getvalue() for f in uploaded_files}
pdf_bytes = generate_batch_report(res_data, files_map)
st.download_button(
label="📄 Download Executive Batch Report (PDF)",
data=pdf_bytes,
file_name=f"PalmOil_BatchReport_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf",
mime="application/pdf",
width='stretch'
)
if st.button("Clear Results & Start New Batch", width='stretch'):
st.session_state.last_batch_results = None
st.rerun()
st.divider()
# 3. Uploader UI
col_batch1, col_batch2 = st.columns([4, 1])
with col_batch1:
uploaded_files = st.file_uploader(
"Upload multiple images...",
type=["jpg", "jpeg", "png"],
accept_multiple_files=True,
key=f"batch_{st.session_state.batch_uploader_key}",
on_change=reset_batch_results
)
with col_batch2:
st.write("##") # Alignment
if st.session_state.last_batch_results is None and uploaded_files:
if st.button("🔍 Process Batch", type="primary", width='stretch'):
with st.spinner(f"Analyzing {len(uploaded_files)} images with {model_type.upper()}..."):
files = [("files", (f.name, f.getvalue(), f.type)) for f in uploaded_files]
payload = {"model_type": model_type}
res = requests.post(f"{API_BASE_URL}/process_batch", files=files, data=payload)
if res.status_code == 200:
data = res.json()
if data["status"] == "success":
st.session_state.last_batch_results = data
st.session_state.batch_uploader_key += 1
st.rerun()
elif data["status"] == "partial_success":
st.warning(data["message"])
st.info(f"Successfully detected {data['detections_count']} bunches locally.")
else:
st.error(f"Batch Error: {data['message']}")
else:
st.error(f"Batch Processing Failed: {res.text}")
if st.button("🗑️ Reset Uploader"):
st.session_state.batch_uploader_key += 1
st.session_state.last_batch_results = None
st.rerun()
# --- Tab 3: Similarity Search ---
with tab3:
st.subheader("Hybrid Semantic Search")
st.markdown("Search records by either **Image Similarity** or **Natural Language Query**.")
with st.form("hybrid_search_form"):
col_input1, col_input2 = st.columns(2)
with col_input1:
search_file = st.file_uploader("Option A: Search Image...", type=["jpg", "jpeg", "png"], key="search")
with col_input2:
text_query = st.text_input("Option B: Natural Language Query", placeholder="e.g., 'ripe bunches with dark spots' or 'unripe fruit'")
top_k = st.slider("Results Limit (Top K)", 1, 20, 3)
submit_search = st.form_submit_button("Run Semantic Search")
if submit_search:
if not search_file and not text_query:
st.warning("Please provide either an image or a text query.")
else:
with st.spinner("Searching Vector Index..."):
payload = {"limit": top_k}
# If an image is uploaded, it takes precedence for visual search
if search_file:
files = {"file": (search_file.name, search_file.getvalue(), search_file.type)}
# Pass top_k as part of the data
res = requests.post(f"{API_BASE_URL}/search_hybrid", files=files, data=payload)
# Otherwise, use text query
elif text_query:
payload["text_query"] = text_query
# Send as form-data (data=) to match FastAPI's Form(None)
res = requests.post(f"{API_BASE_URL}/search_hybrid", data=payload)
if res.status_code == 200:
results = res.json().get("results", [])
if not results:
st.warning("No similar records found.")
else:
st.success(f"Found {len(results)} matches.")
for item in results:
with st.container(border=True):
c1, c2 = st.columns([1, 2])
# Fetch the image for this result
rec_id = item["_id"]
img_res = requests.get(f"{API_BASE_URL}/get_image/{rec_id}")
with c1:
if img_res.status_code == 200:
img_b64 = img_res.json().get("image_data")
if img_b64:
st.image(base64.b64decode(img_b64), width=250)
else:
st.write("No image data found.")
else:
st.write("Failed to load image.")
with c2:
st.write(f"**Class:** {item['ripeness_class']}")
st.write(f"**Similarity Score:** {item['score']:.4f}")
st.write(f"**Timestamp:** {item['timestamp']}")
st.write(f"**ID:** `{rec_id}`")
else:
st.error(f"Search failed: {res.text}")
# --- Tab 4: History Vault ---
with tab4:
st.subheader("📜 Local History Vault")
if "selected_history_id" not in st.session_state:
st.session_state.selected_history_id = None
try:
res = requests.get(f"{API_BASE_URL}/get_history")
if res.status_code == 200:
history_data = res.json().get("history", [])
if not history_data:
st.info("No saved records found.")
else:
if st.session_state.selected_history_id is None:
# ListView Mode
st.write("### 📋 Record List")
df_history = pd.DataFrame(history_data)[['id', 'filename', 'timestamp', 'inference_ms']]
st.dataframe(df_history, hide_index=True, use_container_width=True)
id_to_select = st.number_input("Enter Record ID to view details:", min_value=int(df_history['id'].min()), max_value=int(df_history['id'].max()), step=1)
if st.button("Deep Dive Analysis", type="primary"):
st.session_state.selected_history_id = id_to_select
st.rerun()
else:
# Detail View Mode
record = next((item for item in history_data if item["id"] == st.session_state.selected_history_id), None)
if not record:
st.error("Record not found.")
if st.button("Back to List"):
st.session_state.selected_history_id = None
st.rerun()
else:
if st.button("⬅️ Back to History List"):
st.session_state.selected_history_id = None
st.rerun()
st.divider()
st.write(f"## 🔍 Deep Dive: Record #{record['id']} ({record['filename']})")
detections = json.loads(record['detections'])
summary = json.loads(record['summary'])
# Metrics Row
h_col1, h_col2, h_col3, h_col4 = st.columns(4)
with h_col1:
st.metric("Total Bunches", sum(summary.values()))
with h_col2:
st.metric("Healthy (Ripe)", summary.get('Ripe', 0))
with h_col3:
st.metric("Abnormal Alerts", summary.get('Abnormal', 0))
with h_col4:
st.metric("Inference Speed", f"{record.get('inference_ms', 0) or 0:.1f} ms")
# Image View
if os.path.exists(record['archive_path']):
with open(record['archive_path'], "rb") as f:
hist_img = Image.open(f).convert("RGB")
display_interactive_results(hist_img, detections, key=f"hist_{record['id']}")
else:
st.error(f"Archive file not found: {record['archive_path']}")
# Technical Evidence Expander
with st.expander("🛠️ Technical Evidence: Raw Output Tensor"):
raw_data = record.get('raw_array_sample')
if raw_data:
try:
st.json(json.loads(raw_data))
except:
st.text(raw_data)
else:
st.info("No raw tensor data available for this record.")
else:
st.error(f"Failed to fetch history: {res.text}")
except Exception as e:
st.error(f"Error loading history: {str(e)}")