Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+from ultralytics import YOLO
+from transformers import pipeline
+
+# Load YOLOv8 model (you can use 'yolov8n.pt' for small, or upload a custom model)
+model = YOLO("yolov8n.pt")
+
+# Load language model pipeline (can swap with Groq/LLaMA3 API if needed)
+summarizer = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1")
+
+# Function to detect objects
+def detect_objects(image_path):
+    results = model(image_path)
+    names = results[0].names
+    boxes = results[0].boxes
+    detected = [names[int(cls)] for cls in boxes.cls]
+    return results, detected
+
+# Function to generate report from detected objects
+def generate_report(detected_items):
+    prompt = f"""
+    Generate a construction site report based on the following detected items:
+    {', '.join(detected_items)}.
+
+    Mention safety compliance issues if helmets, vests, or barriers are missing.
+    """
+    output = summarizer(prompt, max_length=250, do_sample=True)[0]["generated_text"]
+    return output
+
+# Streamlit UI
+st.set_page_config(page_title="Photo to Construction Report", layout="centered")
+st.title("📷 Photo to Construction Report Generator")
+
+uploaded_image = st.file_uploader("Upload a construction site photo", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    with open("uploaded.jpg", "wb") as f:
+        f.write(uploaded_image.read())
+
+    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
+
+    with st.spinner("Running object detection..."):
+        results, detected_items = detect_objects("uploaded.jpg")
+        st.image(results[0].plot(), caption="Detected Objects", use_column_width=True)
+
+    with st.spinner("Generating AI report..."):
+        report = generate_report(detected_items)
+        st.subheader("📄 AI-Generated Report")
+        st.write(report)