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# Import necessary libraries
import os
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification
import gradio as gr
import openai
# Load the Hugging Face model for car damage detection
model_name = "beingamit99/car_damage_detection"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
# Set your OpenAI API key
openai_api_key = os.getenv("OpenAI4oMini")
# Validate API Key
if openai_api_key is None:
raise ValueError("OpenAI API key is not set. Make sure to set the OpenAI4oMini secret in Hugging Face.")
# Initialize OpenAI Client
client = openai.OpenAI(api_key=openai_api_key)
# Dropdown Options
car_companies = ["Select", "Toyota", "Honda", "Ford", "BMW", "Mercedes", "Audi", "Hyundai", "Kia", "Nissan"]
car_models = [
"Select", # Default option
"Corolla", "Camry", "RAV4", "Highlander", # Toyota
"Civic", "Accord", "CR-V", "Pilot", # Honda
"Fiesta", "Focus", "Explorer", "Mustang", # Ford
"3 Series", "5 Series", "X3", "X5", # BMW
"C-Class", "E-Class", "GLC", "GLE", # Mercedes
"A3", "A4", "Q5", "Q7", # Audi
"Elantra", "Sonata", "Tucson", "Santa Fe", # Hyundai
"Rio", "Optima", "Sportage", "Sorento", # Kia
"Sentra", "Altima", "Rogue", "Murano" # Nissan
]
years = [str(year) for year in range(2000, 2025)]
countries = ["Select", "Pakistan", "USA", "UK", "Canada", "Australia", "Germany", "India", "Japan"]
# Function to Estimate Repair Cost using GPT-4.0 Mini
def estimate_repair_cost(damage_type, company, model, year, country):
prompt = (
f"Estimate the repair cost for {damage_type} on a {year} {company} {model} in {country}. "
f"Provide the approximate total cost in local currency with your confidence level, concisely in 2 lines."
)
try:
# Using client for API call
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "You are an expert in car repair cost estimation."},
{"role": "user", "content": prompt}
],
temperature=0.5,
max_tokens=100
)
# Correctly access the response content
return response.choices[0].message.content.strip()
except Exception as e:
print(f"Error in GPT-4.0 API call: {e}")
return f"Error: {e}"
# Function to Detect Car Damage from Image using Hugging Face Model
def detect_damage(image):
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
confidences, predicted_class = torch.max(probs, dim=-1)
predicted_label = model.config.id2label[predicted_class.item()]
return predicted_label, confidences.item()
# Function to Process Image and Get Results
def process_image(image, company, model, year, country):
damage_type, confidence = detect_damage(image)
cost_estimate = estimate_repair_cost(damage_type, company, model, year, country)
result = {
"Major Detected Damage": damage_type,
"Confidence": f"{confidence * 100:.2f}%",
"Estimated Repair Cost": cost_estimate
}
return result
# Gradio Interface
with gr.Blocks() as interface:
gr.Markdown("# Car Damage Detection and Cost Estimation")
gr.Markdown("Upload an image of a damaged car to detect the type of damage and estimate the repair cost.")
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil", label="Upload Car Image")
company_input = gr.Dropdown(choices=car_companies, label="Car Company", value="Select")
model_input = gr.Dropdown(choices=car_models, label="Car Model", value="Select")
year_input = gr.Dropdown(choices=years, label="Year of Manufacture", value=years[-1])
country_input = gr.Dropdown(choices=countries, label="Your Country", value="Select")
submit_button = gr.Button("Estimate Repair Cost")
output = gr.JSON(label="Result")
submit_button.click(process_image, inputs=[image_input, company_input, model_input, year_input, country_input], outputs=output)
# Launch the Gradio Interface
interface.launch()
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