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# main.py
import os
import gradio as gr
from PIL import Image
import io
import base64
from groq import Groq
# Initialize Groq client with API key (set this as a secret in HF Spaces)
client = Groq(api_key=os.environ.get("construction"))
# Helper: Convert PIL Image to base64
def image_to_base64(image):
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode()
# Prompt for model
SYSTEM_PROMPT = """
You are a helpful civil engineering assistant. The user uploads an image showing some construction damage such as cracks, water leakage, or pipe failure. Based on the image, give:
1. Likely issue
2. Possible solution
3. Tools or materials needed
4. Estimated time to fix
Use simple, helpful, practical language.
"""
# Chatbot logic
def analyze_image(image, history):
if image is None:
return history + [("User", "No image uploaded."), ("Bot", "Please upload a damage photo.")]
base64_img = image_to_base64(image)
image_url = f"data:image/jpeg;base64,{base64_img}"
try:
response = client.chat.completions.create(
model="meta-llama/llama-4-scout-17b-16e-instruct",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": [
{"type": "text", "text": "Please analyze this image and give advice on the damage."},
{"type": "image_url", "image_url": {"url": image_url}}
]}
],
temperature=0.7,
max_tokens=512
)
reply = response.choices[0].message.content
history.append(("User", "Uploaded image"))
history.append(("Bot", reply))
return history
except Exception as e:
return history + [("Bot", f"❌ Error: {str(e)}")]
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("## 🛠️ Construction Damage Assistant\nUpload a photo of damage to get repair advice.")
with gr.Row():
with gr.Column(scale=1):
image_input = gr.Image(type="pil", label="Upload Damage Image")
with gr.Column(scale=2):
chatbot = gr.Chatbot(label="Repair Suggestions", height=450)
state = gr.State([])
submit_btn = gr.Button("Analyze")
submit_btn.click(fn=analyze_image, inputs=[image_input, state], outputs=chatbot)
demo.launch()
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