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import os
import streamlit as st
import cv2
import numpy as np
from PIL import Image
from ultralytics import YOLO
from groq import Groq
# Load API key from Streamlit secrets
groq_api_key = st.secrets["GROQ_API_KEY"]
# Initialize Groq client
client = Groq(api_key=groq_api_key)
# Load YOLOv8 model (pretrained on COCO dataset)
model = YOLO("yolov8n.pt")
# Function to get AI-powered recommendations
def get_ai_recommendation(detected_objects):
if not detected_objects:
return "No objects detected. Please try again with a clearer image."
prompt = f"Suggest optimal placement and design improvements for: {', '.join(detected_objects)} in a room."
try:
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": prompt}],
model="llama-3.3-70b-versatile"
)
return chat_completion.choices[0].message.content
except Exception as e:
return f"Error fetching AI recommendation: {e}"
# Streamlit UI
st.title("🏡 AI-Powered Home Setup Chatbot")
st.write("Upload an image of your room or use your camera for live capture.")
# Upload image option
uploaded_file = st.file_uploader("Upload an image of your room:", type=["jpg", "jpeg", "png"])
# Live camera capture
use_camera = st.checkbox("Use Live Camera")
if use_camera:
st.write("Click 'Capture Image' to take a photo.")
camera = cv2.VideoCapture(0) # Open the webcam
if st.button("Capture Image"):
ret, frame = camera.read()
if ret:
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
captured_image = Image.fromarray(image)
st.image(captured_image, caption="Captured Image", use_column_width=True)
# Run YOLOv8 for object detection
results = model(image)
detected_objects = [model.names[int(box.cls)] for box in results[0].boxes]
# Display detected objects
st.subheader("Detected Objects:")
st.write(", ".join(set(detected_objects)))
# Get AI-powered recommendations
st.subheader("AI-Powered Placement Suggestions:")
ai_recommendations = get_ai_recommendation(detected_objects)
st.write(ai_recommendations)
else:
st.error("Failed to capture image. Please check your camera and try again.")
camera.release()
cv2.destroyAllWindows()
elif uploaded_file:
image = Image.open(uploaded_file)
image = np.array(image)
# Run YOLOv8 for object detection
results = model(image)
detected_objects = [model.names[int(box.cls)] for box in results[0].boxes]
# Display uploaded image and detected objects
st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
st.subheader("Detected Objects:")
st.write(", ".join(set(detected_objects)))
# Get AI-powered recommendations
st.subheader("AI-Powered Placement Suggestions:")
ai_recommendations = get_ai_recommendation(detected_objects)
st.write(ai_recommendations)
# Text-based recommendations
user_input = st.text_input("Or enter an object name:")
if user_input:
ai_recommendation = get_ai_recommendation([user_input.lower()])
st.subheader("AI-Powered Suggestion:")
st.write(ai_recommendation)
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