Initial Commit

app.py

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+# Import necessary libraries
+import os
+import requests
+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")
+
+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:
+        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
+        )
+        return response.choices[0].message.content.strip()
+    except Exception as e:
+        print(f"Error in GPT-4o 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()

requirements.txt

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+transformers
+torch
+gradio
+Pillow
+requests
+openai