Update app.py

app.py

@@ -1,7 +1,7 @@
 # Import necessary libraries
 import os
-import torch
 from PIL import Image
+import torch
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 import gradio as gr
 import openai
@@ -11,10 +11,15 @@ model_name = "beingamit99/car_damage_detection"
 processor = AutoImageProcessor.from_pretrained(model_name)
 model = AutoModelForImageClassification.from_pretrained(model_name)
 
-# Set your OpenAI API key from environment variable
+# Set your OpenAI API key
 openai_api_key = os.getenv("OpenAI4oMini")
-openai.api_key = openai_api_key  # Correct way to set the API key
-client = openai.OpenAI(api_key=openai_api_key)  # Ensure client is initialized
+
+# 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"]
@@ -37,12 +42,13 @@ countries = ["Select", "Pakistan", "USA", "UK", "Canada", "Australia", "Germany"
 # 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."
-)
+        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(
+        # Using client for API call
+        response = client.ChatCompletion.create(
             model="gpt-4o-mini",
             messages=[
                 {"role": "system", "content": "You are an expert in car repair cost estimation."},
@@ -51,6 +57,7 @@ def estimate_repair_cost(damage_type, company, model, year, country):
             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}")
@@ -85,11 +92,11 @@ with gr.Blocks() as interface:
     
     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")
+            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")