Update app.py

app.py

@@ -1,16 +1,35 @@
-import gradio as gr
+from dotenv import load_dotenv, find_dotenv
+load_dotenv(find_dotenv())
+
+import os # Provides a way of using operating system-dependent functionality
+import io # Provides core tools for working with streams of data
+from io import BytesIO
+import IPython.display # Used for displaying rich content (e.g., images, HTML) in Jupyter Notebooks
+from PIL import Image # Python Imaging Library for opening, manipulating, and saving image files
+import base64 # Encodes and decodes data in base64 format
 import requests
-from PIL import Image
-import os
+import json
+import torch
+import torch.nn as nn
+import warnings
+import gradio as gr
+
+# Ignore specific UserWarnings related to max_length in transformers
+warnings.filterwarnings("ignore", message=".*Using the model-agnostic default `max_length`.*")
+
+# Load environment variables from .env file
+hf_api_key = os.getenv('API_TOKEN')
+endpoint_url = os.getenv('INFERENCE_ENDPOINT')
+
 
 # Set your Inference Endpoint URL and API key
-INFERENCE_ENDPOINT = "https://your-endpoint-url"  # Replace with your endpoint URL
-API_TOKEN = "your-api-token"  # Replace with your Hugging Face API token
+#INFERENCE_ENDPOINT = "https://your-endpoint-url"  # Replace with your endpoint URL
+#API_TOKEN = "your-api-token"  # Replace with your Hugging Face API token
 
-#Image-to-text endpoint
-def get_completion(inputs, parameters=None, endpoint_url=INFERENCE_ENDPOINT):
+#Image-to-text endpoint - Helper funcion 
+def get_completion(inputs, parameters=None, endpoint_url=endpoint_url):
     headers = {
-        "Authorization": f"Bearer {API_TOKEN}",
+        "Authorization": f"Bearer {hf_api_key}",
         "Content-Type": "application/json"
     }
     data = {"inputs": inputs}
@@ -27,16 +46,8 @@ def get_generation(model, processor, image, dtype):
 def load_image(img_url):
     image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
     return image
-
-#Open the images
-#Image1=Image.open('dlaima/Multiple_Image_captioning/main/image1.jpg')
-#Image2=Image.open('https://huggingface.co/spaces/dlaima/Multiple_Image_captioning/resolve/main/image2.jpeg')
-#Image3=Image.open('https://huggingface.co/spaces/dlaima/Multiple_Image_captioning/resolve/main/image3.jpeg')
-
-#image_url = 'https://free-images.com/lg/9e46/white_bengal_tiger_tiger_0.jpg'
-#image = load_image(image_url)
-
-
+    
+#Gradio interface 
 def caption_image(image_url):
     # Download the image from the URL
     response = requests.get(image_url)
@@ -48,7 +59,6 @@ def caption_image(image_url):
     caption = get_completion(image_url)
     return caption
 
-
 # Gradio interface
 
 demo = gr.Interface(