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6aa05f9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import requests
import torch
import os
# Load your Groq API key from Hugging Face Space secrets
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
# Load BLIP model for image captioning
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to("cuda" if torch.cuda.is_available() else "cpu")
# Function to generate image caption using BLIP
def generate_caption(image):
inputs = processor(image, return_tensors="pt").to(model.device)
output = model.generate(**inputs)
caption = processor.decode(output[0], skip_special_tokens=True)
return caption
# Function to get a repair suggestion from Groq API
def ask_groq(caption, question):
prompt = f"""The following image caption describes some construction damage: "{caption}"
The user has a question about the damage: "{question}"
Based on the caption and the question, provide:
- Type of damage
- Tools needed
- Estimated repair time
- Step-by-step repair guidance
Be as detailed and practical as possible.
"""
headers = {
"Authorization": f"Bearer {GROQ_API_KEY}",
"Content-Type": "application/json"
}
body = {
"messages": [
{"role": "system", "content": "You are a helpful civil engineer and construction repair assistant."},
{"role": "user", "content": prompt}
],
"model": "llama3-70b-8192"
}
response = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=body)
if response.status_code == 200:
return response.json()["choices"][0]["message"]["content"]
else:
return f"Error: {response.status_code} - {response.text}"
# Main pipeline
def process_image_and_question(image, question):
if image is None:
return "Please upload an image."
if not question:
return "Please enter a question about the damage."
caption = generate_caption(image)
response = ask_groq(caption, question)
return f"📸 Image Caption: {caption}\n\n💬 Response:\n{response}"
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## 🏗️ Construction Damage Assistant")
gr.Markdown("Upload an image and ask a repair question. The AI will identify the issue and help you fix it.")
with gr.Row():
image = gr.Image(label="📷 Upload Damage Image", type="pil")
question = gr.Textbox(label="💬 Your Question", placeholder="What tools do I need? How long will it take?")
submit_btn = gr.Button("Get Repair Advice")
output = gr.Textbox(label="🛠️ AI Response", lines=15)
submit_btn.click(fn=process_image_and_question, inputs=[image, question], outputs=output)
# Launch the app
demo.launch()
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