Create app.py

app.py

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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)