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import streamlit as st
from PIL import Image, UnidentifiedImageError
import io
import time
import os
import random
import json # For API simulation
import requests # To simulate API calls (though we won't make real ones here)
import uuid # For job IDs
# ------------------------------------------------------------------------------
# Page Configuration
# ------------------------------------------------------------------------------
st.set_page_config(
page_title="AI Real Estate Visualization Suite [PRO]",
layout="wide",
page_icon="🚀",
initial_sidebar_state="expanded"
)
# ------------------------------------------------------------------------------
# Simulated Backend API Interaction
# ------------------------------------------------------------------------------
# Replace with your actual backend API endpoint
BACKEND_API_URL = "http://your-production-backend.com/api/v1/visualize"
# Replace with your actual status check endpoint
STATUS_API_URL = "http://your-production-backend.com/api/v1/status/{job_id}"
# --- Simulate API Call ---
def submit_visualization_job(payload: dict) -> tuple[str | None, str | None]:
"""
Simulates submitting a job to the backend API.
In reality, this would use 'requests.post'.
Args:
payload (dict): Data to send (params, input reference, etc.)
Returns:
tuple[str | None, str | None]: (job_id, error_message)
"""
st.info("Submitting job to backend simulation...")
print(f"SIMULATING API SUBMIT to {BACKEND_API_URL}")
print("Payload (summary):")
print(f" Mode: {payload.get('output_mode')}")
print(f" Style: {payload.get('style')}")
print(f" Input Type: {payload.get('input_type')}")
# Omit potentially large data like image bytes from console log in real scenario
# print(json.dumps(payload, indent=2)) # Don't print potentially large data
# Simulate network latency & backend processing start
time.sleep(1.5)
# Simulate success/failure
if random.random() < 0.95: # 95% success rate
job_id = f"job_{uuid.uuid4()}"
print(f"API Submit Simulation SUCCESS: Job ID = {job_id}")
# In a real app, store job_id associated with user/session
return job_id, None
else:
error_msg = "Simulated API Error: Failed to submit job (e.g., server busy, invalid params)."
print(f"API Submit Simulation FAILED: {error_msg}")
return None, error_msg
# --- Simulate Status Check ---
def check_job_status(job_id: str) -> tuple[str, str | None, str | None]:
"""
Simulates checking the status of a job via the backend API.
In reality, this would use 'requests.get'.
Args:
job_id (str): The ID of the job to check.
Returns:
tuple[str, str | None, str | None]: (status, result_url, error_message)
status: 'PENDING', 'PROCESSING', 'COMPLETED', 'FAILED'
"""
status_url = STATUS_API_URL.format(job_id=job_id)
print(f"SIMULATING API STATUS CHECK for {job_id} at {status_url}")
time.sleep(0.5) # Simulate network latency
# Simulate different states based on time or random chance
# This is highly simplified; a real backend manages state.
if job_id not in st.session_state.job_progress:
st.session_state.job_progress[job_id] = 0 # Start progress counter
st.session_state.job_progress[job_id] += random.uniform(0.1, 0.3) # Increment progress
if st.session_state.job_progress[job_id] < 0.2:
status = "PENDING"
elif st.session_state.job_progress[job_id] < 0.8:
status = "PROCESSING"
elif st.session_state.job_progress[job_id] < 1.0:
# Simulate occasional failure during processing
if random.random() < 0.05: # 5% chance of failure during processing
print(f"API Status Simulation: Job {job_id} FAILED during processing.")
st.session_state.job_progress[job_id] = 99 # Mark as failed state
return "FAILED", None, "Simulated AI Processing Error."
else:
status = "PROCESSING" # Still processing
else:
status = "COMPLETED"
if status == "COMPLETED":
# Simulate getting a result URL (e.g., to a generated image in cloud storage)
# Use a *real placeholder path* accessible by the Streamlit app for the demo
result_placeholder = "assets/staged_result_placeholder.png" # Make sure this exists!
print(f"API Status Simulation: Job {job_id} COMPLETED. Result URL (simulated): {result_placeholder}")
return "COMPLETED", result_placeholder, None
elif status == "FAILED":
print(f"API Status Simulation: Job {job_id} FAILED.")
# Error message might have been set earlier
error = st.session_state.job_errors.get(job_id, "Unknown processing error.")
return "FAILED", None, error
else:
print(f"API Status Simulation: Job {job_id} is {status}.")
return status, None, None
# --- Simulate Fetching Result Image ---
def fetch_result_image(image_path_or_url: str) -> Image.Image | None:
"""
Simulates fetching the result image from a URL or path.
In reality, this would use requests.get for a URL.
For this demo, we just load from the local placeholder path.
"""
print(f"SIMULATING Fetching result image from: {image_path_or_url}")
if os.path.exists(image_path_or_url):
try:
img = Image.open(image_path_or_url).convert("RGB")
return img
except UnidentifiedImageError:
print(f"ERROR: Placeholder at '{image_path_or_url}' is not a valid image.")
return None
except Exception as e:
print(f"ERROR: Failed to load placeholder '{image_path_or_url}': {e}")
return None
else:
print(f"ERROR: Placeholder image not found at '{image_path_or_url}'.")
return None
# ------------------------------------------------------------------------------
# Authentication Simulation
# ------------------------------------------------------------------------------
def show_login_form():
st.warning("Please log in to use the Visualization Suite.")
with st.form("login_form"):
username = st.text_input("Username")
password = st.text_input("Password", type="password")
submitted = st.form_submit_button("Login")
if submitted:
# --- !!! WARNING: NEVER use hardcoded passwords in production !!! ---
# --- This is purely for demonstration. Use secure auth libraries ---
# --- like streamlit-authenticator or integrate with OAuth/etc. ---
if username == "admin" and password == "password123":
st.session_state.logged_in = True
st.session_state.username = username
st.success("Login successful!")
time.sleep(1) # Give user time to see success message
st.rerun() # Rerun to show the main app
else:
st.error("Invalid username or password.")
# ------------------------------------------------------------------------------
# Initialize Session State (More Robust)
# ------------------------------------------------------------------------------
def initialize_state():
defaults = {
'logged_in': False,
'username': None,
'input_data_bytes': None, # Store raw bytes
'input_image_preview': None, # Store PIL for display if image
'input_type': None,
'uploaded_filename': None,
'current_job_id': None,
'job_status': None, # PENDING, PROCESSING, COMPLETED, FAILED
'job_progress': {}, # Dict to track progress simulation per job_id
'job_errors': {}, # Dict to store errors per job_id
'ai_result_image': None,
'last_run_params': {}
}
for key, value in defaults.items():
if key not in st.session_state:
st.session_state[key] = value
initialize_state()
# ------------------------------------------------------------------------------
# Main Application Logic
# ------------------------------------------------------------------------------
# --- Authentication Gate ---
if not st.session_state.logged_in:
show_login_form()
st.stop() # Stop execution if not logged in
# --- Main App UI (if logged in) ---
st.title("🚀 AI Real Estate Visualization Suite [PRO]")
st.caption(f"Welcome, {st.session_state.username}! Advanced AI tools at your fingertips.")
st.markdown("---")
# --- Sidebar ---
with st.sidebar:
st.header("⚙️ Input & Configuration")
st.caption(f"User: {st.session_state.username}")
if st.button("Logout"):
for key in list(st.session_state.keys()): # Clear state on logout
del st.session_state[key]
initialize_state() # Re-init default state
st.rerun()
st.markdown("---")
# Prevent changing input while a job is running
input_disabled = st.session_state.job_status in ["PENDING", "PROCESSING"]
input_file = st.file_uploader(
"1. Upload File",
type=["png", "jpg", "jpeg", "webp", "dxf", "dwg", "obj", "fbx"], # Add more as needed
key="file_uploader",
accept_multiple_files=False,
help="Upload Room Image, Floor Plan (DXF - simulated), or 3D Model (OBJ - simulated).",
disabled=input_disabled
)
# --- Input Processing ---
if input_file is not None:
if input_file.name != st.session_state.uploaded_filename:
st.info(f"Processing '{input_file.name}'...")
file_ext = os.path.splitext(input_file.name)[1].lower()
file_bytes = input_file.getvalue()
processed = False
input_image_preview = None
try:
if file_ext in ['.png', '.jpg', '.jpeg', '.webp']:
image = Image.open(io.BytesIO(file_bytes)).convert("RGB")
max_size = (1024, 1024)
image.thumbnail(max_size, Image.Resampling.LANCZOS)
input_image_preview = image # Store PIL for preview
input_type = 'image'
processed = True
elif file_ext in ['.dxf', '.dwg']:
input_type = 'floorplan'
processed = True
st.warning("Floor plan processing is simulated.")
elif file_ext in ['.obj', '.fbx']:
input_type = '3dmodel'
processed = True
st.warning("3D model processing is simulated.")
else:
st.error(f"Unsupported file type: {file_ext}")
if processed:
st.session_state.input_data_bytes = file_bytes # Store bytes for API
st.session_state.input_image_preview = input_image_preview
st.session_state.input_type = input_type
st.session_state.uploaded_filename = input_file.name
# Reset job state
st.session_state.current_job_id = None
st.session_state.job_status = None
st.session_state.ai_result_image = None
st.session_state.last_run_params = {}
st.success(f"File '{input_file.name}' ({input_type}) loaded.")
st.rerun() # Rerun to reflect loaded state
else:
st.session_state.input_data_bytes = None
st.session_state.input_image_preview = None
st.session_state.input_type = None
st.session_state.uploaded_filename = None
except UnidentifiedImageError:
st.error("Uploaded file is not a valid image or is corrupted.")
except Exception as e:
st.error(f"Error processing file: {e}")
st.session_state.input_data_bytes = None
st.session_state.input_image_preview = None
st.session_state.input_type = None
st.session_state.uploaded_filename = None
# --- Configuration Options ---
if st.session_state.input_type is not None:
st.markdown("---")
st.subheader("2. Visualization Mode")
output_mode = st.selectbox(
"Select Mode:",
options=['Virtual Staging', 'Renovation Preview', 'Material Swap', 'Layout Variation'],
key='output_mode_select',
disabled=input_disabled
).lower().replace(' ', '_')
st.markdown("---")
st.subheader("3. Scene Parameters")
room_type = st.selectbox("Room Type:", ["Living Room", "Bedroom", "Kitchen", "Dining Room", "Office", "Bathroom", "Other"], key="room_select", disabled=input_disabled)
style = st.selectbox("Primary Style:", ["Modern", "Contemporary", "Minimalist", "Scandinavian", "Industrial", "Traditional", "Coastal", "Farmhouse", "Bohemian"], key="style_select", disabled=input_disabled)
# Advanced Controls
with st.expander("✨ Advanced Controls", expanded=False):
furniture_prefs = st.text_input("Furniture Preferences", placeholder="e.g., 'Large velvet green sofa'", key="furniture_input", disabled=input_disabled)
wall_color = st.color_picker("Wall Color (Approx.)", value="#FFFFFF", key="wall_color_picker", disabled=input_disabled)
floor_type = st.selectbox("Floor Type", ["(Auto)", "Oak Wood", "Dark Wood", "Light Wood", "Carpet (Neutral)", "Concrete", "Tile (Light)", "Tile (Dark)"], key="floor_select", disabled=input_disabled)
lighting_time = st.slider("Time of Day", 0.0, 1.0, 0.5, 0.1, "%.1f", key="lighting_slider", help="0.0=Dawn, 0.5=Midday, 1.0=Dusk", disabled=input_disabled)
camera_angle = st.selectbox("Camera Angle", ["Eye-Level", "High Angle", "Low Angle", "Wide (Simulated)"], key="camera_select", disabled=input_disabled)
remove_objects = st.checkbox("Attempt Remove Existing Objects", value=False, key="remove_obj_check", disabled=input_disabled)
renovation_instructions = ""
if output_mode == 'renovation_preview':
renovation_instructions = st.text_input("Renovation Instructions", placeholder="e.g., 'Remove center wall'", key="renovation_input", disabled=input_disabled)
st.markdown("---")
st.subheader("4. Generate Visualization")
# Disable button if no input or job already running
disable_generate = st.session_state.input_type is None or st.session_state.job_status in ["PENDING", "PROCESSING"]
if st.button("🚀 Submit Visualization Job", key="generate_button", use_container_width=True, disabled=disable_generate):
# Prepare payload for the backend
# In production, you'd likely upload the file to S3 first and send a URL/key
# For image data, you might send base64 encoded string or upload separately
payload = {
"user_id": st.session_state.username, # Identify the user
# "input_reference": "s3://bucket/user_uploads/filename.jpg", # Example
"input_filename": st.session_state.uploaded_filename, # For info
"input_type": st.session_state.input_type,
"output_mode": output_mode,
"room_type": room_type,
"style": style,
"furniture_prefs": furniture_prefs,
"materials": {'wall_color': wall_color, 'floor_type': floor_type},
"lighting_time": lighting_time,
"camera_angle": camera_angle,
"remove_objects": remove_objects,
"renovation_instructions": renovation_instructions,
}
# Attach input data - simplistic approach for demo, REAL API would handle uploads better
# Never send large files directly in JSON payload in production!
# payload['input_data_b64'] = base64.b64encode(st.session_state.input_data_bytes).decode()
job_id, error = submit_visualization_job(payload)
if job_id:
st.session_state.current_job_id = job_id
st.session_state.job_status = "PENDING"
st.session_state.ai_result_image = None # Clear previous result
st.session_state.last_run_params = payload # Store params for display
st.session_state.job_progress = {job_id: 0} # Reset progress for new job
st.session_state.job_errors = {} # Clear old errors
st.success(f"Job submitted successfully! Job ID: {job_id}")
st.rerun() # Start polling
else:
st.error(f"Failed to submit job: {error}")
st.session_state.current_job_id = None
st.session_state.job_status = "FAILED" # Mark status
else:
st.info("⬆️ Upload a file to begin.")
# --- Main Display Area ---
col1, col2 = st.columns(2)
with col1:
st.subheader("Input")
if st.session_state.input_type is not None:
if st.session_state.input_type == 'image' and st.session_state.input_image_preview:
st.image(st.session_state.input_image_preview, caption=f"Input: {st.session_state.uploaded_filename}", use_column_width=True)
elif st.session_state.input_type != 'image':
# Display info for non-image types
st.info(f"{st.session_state.input_type.capitalize()} File:\n**{st.session_state.uploaded_filename}**")
st.caption("(Preview not available for non-image inputs in this demo)")
else:
st.warning("Input loaded, but preview unavailable.")
else:
st.markdown("<div style='height: 400px; border: 2px dashed #ccc; ...'>Upload Input File</div>", unsafe_allow_html=True)
with col2:
st.subheader("AI Visualization Result")
job_id = st.session_state.current_job_id
status = st.session_state.job_status
if job_id:
# --- Job Status Display ---
if status == "PENDING":
st.info(f"Job Status: Pending... (ID: {job_id})")
# Trigger periodic rerun for polling
time.sleep(5) # Poll every 5 seconds (adjust as needed)
st.rerun()
elif status == "PROCESSING":
progress_value = st.session_state.job_progress.get(job_id, 0)
st.progress(min(progress_value, 1.0), text=f"Job Status: Processing... ({int(min(progress_value, 1.0)*100)}%) (ID: {job_id})")
# Trigger periodic rerun for polling
time.sleep(3) # Poll faster while processing
st.rerun()
elif status == "COMPLETED":
st.success(f"Job Status: Completed! (ID: {job_id})")
if st.session_state.ai_result_image:
run_mode_display = st.session_state.last_run_params.get('output_mode', 'N/A').replace('_', ' ').title()
st.image(st.session_state.ai_result_image, caption=f"Result ({run_mode_display})", use_column_width=True)
# Download Button logic (similar to before)
# ... (Add download button code here) ...
else:
st.error("Job completed, but failed to load the result image.")
with st.expander("View Parameters Used", expanded=False):
# Display JSON but remove potentially large data fields first
params_to_display = {k:v for k,v in st.session_state.last_run_params.items()} # if k != 'input_data_b64'}
st.json(params_to_display, expanded=True)
elif status == "FAILED":
error_msg = st.session_state.job_errors.get(job_id, "An unknown error occurred.")
st.error(f"Job Status: Failed! (ID: {job_id})\nError: {error_msg}")
with st.expander("View Parameters Used", expanded=False):
params_to_display = {k:v for k,v in st.session_state.last_run_params.items()} # if k != 'input_data_b64'}
st.json(params_to_display, expanded=True)
# --- Logic to update status (if job is active) ---
if status in ["PENDING", "PROCESSING"]:
new_status, result_url, error = check_job_status(job_id)
st.session_state.job_status = new_status
if error:
st.session_state.job_errors[job_id] = error
if new_status == "COMPLETED" and result_url:
# Fetch the result image only when completed
st.session_state.ai_result_image = fetch_result_image(result_url)
if st.session_state.ai_result_image is None:
st.session_state.job_status = "FAILED" # Mark as failed if image fetch fails
st.session_state.job_errors[job_id] = "Failed to retrieve/load result image."
elif new_status == "FAILED" and not error: # If status is failed but no error stored yet
st.session_state.job_errors[job_id] = "Job failed for an unknown reason."
# Rerun immediately if status changed significantly to update UI
if status != new_status and new_status in ["COMPLETED", "FAILED"]:
st.rerun()
else: # No active job
st.markdown("<div style='height: 400px; border: 2px dashed #ccc; ...'>Result will appear here</div>", unsafe_allow_html=True)
st.markdown("---")
st.warning("""
**Disclaimer:** This is a **conceptual blueprint** for a production front-end.
User authentication is **not secure**. Backend API calls, job handling, status polling, and AI processing are **simulated**.
Building the actual backend infrastructure and state-of-the-art AI models requires significant resources and expertise.
""")