PalmOilRipenessDetection-Python/demo_app.py

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

# --- 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()

# Load YOLO model locally for Analytical View
@st.cache_resource
def load_yolo():
    return YOLO('best.pt')

yolo_model = load_yolo()

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", 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")

# Synchronized Slider
st.sidebar.slider(
    "Confidence Threshold", 
    0.1, 1.0, 
    value=float(current_conf),
    key="conf_slider",
    on_change=update_confidence
)

# 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

# --- Tabs ---
tab1, tab2, tab3 = st.tabs(["Single Analysis", "Batch Processing", "Similarity Search"])

# --- 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("Processing Detections Locally..."):
                files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)}
                res = requests.post(f"{API_BASE_URL}/analyze", files=files)
                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()
            
            # SIDE-BY-SIDE ANALYTICAL VIEW
            col_left, col_right = st.columns(2)
            
            # Fetch data once
            data = st.session_state.last_detection

            with col_left:
                st.image(uploaded_file, caption="Original Photo", width='stretch')
            
            with col_right:
                # Use the local model to plot the boxes directly
                img = Image.open(uploaded_file)
                results = yolo_model(img, conf=current_conf, agnostic_nms=True, iou=0.4)
                annotated_img = results[0].plot() # Draws boxes/labels
                
                # Convert BGR (OpenCV format) to RGB for Streamlit
                annotated_img_rgb = annotated_img[:, :, ::-1] 
                st.image(annotated_img_rgb, caption="AI Analytical View (X-Ray)", width='stretch')

            st.write("### 📈 Manager's Dashboard")
            m_col1, m_col2, m_col3 = st.columns(3)
            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")

            col1, col2 = st.columns([1.5, 1]) # Keep original col structure for summary below
            
            with col2:
                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"**{det['class']}** - {det['confidence']:.2%} confidence")
                        
                        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={
                                             'Abnormal': '#ef4444', # Red
                                             'Empty_Bunch': '#94a3b8', # Gray
                                             'Ripe': '#22c55e', # Green
                                             'Underripe': '#eab308', # Yellow
                                             'Unripe': '#3b82f6', # Blue
                                             'Overripe': '#a855f7' # Purple
                                         },
                                         hole=0.4)
                            fig.update_layout(margin=dict(t=0, b=0, l=0, r=0), height=300)
                            st.plotly_chart(fig, width='stretch')

                        # 💡 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")

# --- 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={
                                             'Abnormal': '#ef4444', 
                                             'Empty_Bunch': '#94a3b8', 
                                             'Ripe': '#22c55e'
                                          })
                        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')

            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'])
            if st.button("Clear Results & Start New Batch"):
                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..."):
                    files = [("files", (f.name, f.getvalue(), f.type)) for f in uploaded_files]
                    res = requests.post(f"{API_BASE_URL}/process_batch", files=files)
                    
                    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}")