Spaces:
Sleeping
Sleeping
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. | |
""") |