Create app.py

app.py

@@ -0,0 +1,45 @@
+# app.py
+
+import gradio as gr
+from PIL import Image
+import torch
+from transformers import pipeline
+import os
+
+# Load YOLOv5 model from Ultralytics
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+
+# Load GPT-2 text generation pipeline
+text_generator = pipeline('text-generation', model='gpt2')
+
+def generate_report(image):
+    # Run YOLOv5 detection
+    results = model(image)
+    labels = results.pandas().xyxy[0]['name'].tolist()
+
+    if not labels:
+        return "No relevant objects detected in the image. Please try a clearer construction site photo."
+
+    # Prepare prompt for report generation
+    prompt = (
+        f"The following objects were detected at the construction site: {', '.join(labels)}.\n"
+        "Based on this, write a brief site progress report or highlight any visible safety concerns."
+    )
+
+    # Generate report using GPT-2
+    report = text_generator(prompt, max_length=150, do_sample=True, temperature=0.7)[0]['generated_text']
+
+    return report
+
+# Gradio interface
+interface = gr.Interface(
+    fn=generate_report,
+    inputs=gr.Image(type="pil", label="Upload Construction Site Photo"),
+    outputs=gr.Textbox(label="AI Generated Report"),
+    title="📸 Photo to Construction Report Generator",
+    description="Upload a construction site photo and get an AI-generated progress report or safety checklist based on detected objects.",
+    theme="default"
+)
+
+if __name__ == "__main__":
+    interface.launch()