Update app.py

app.py

@@ -1,56 +1,63 @@
+import base64
+import requests
 import gradio as gr
-from openai import OpenAI
-import os
 from PIL import Image
 import numpy as np
 from datetime import datetime
+import os
 
-# Initialize OpenAI client
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-
-def array_to_image_path(image_array):
-    if image_array is None:
-        raise ValueError("No image provided. Please upload an image before submitting.")
+# OpenAI API Key
+api_key = os.getenv("OPENAI_API_KEY")
 
+# Function to encode the image
+def encode_image(image_array):
+    # Convert numpy array to an image file and encode it in base64
     img = Image.fromarray(np.uint8(image_array))
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"image_{timestamp}.png"
-    img.save(filename)
-    full_path = os.path.abspath(filename)
-    return full_path
-
+    img_buffer = os.path.join("/tmp", f"temp_image_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jpg")
+    img.save(img_buffer, format="JPEG")
+    
+    with open(img_buffer, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")
 
 # Function to generate product description using OpenAI API
 def generate_product_description(image, text_input=None):
-    # Convert the image to a path (simulated handling)
-    image_path = array_to_image_path(image)
+    # Encode the uploaded image
+    base64_image = encode_image(image)
 
-    # Use a generated link from the Gradio app
-    # If share=True is set, the image will be available via a public URL
-    # Make sure the provided URL is accessible and correct in context
-    image_url = image_path  # Replace this with actual URL when using Gradio's share functionality
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}"
+    }
 
-    # API request
-    completion = client.chat.completions.create(
-        model="gpt-4o",
-        messages=[
+    # Payload with base64 encoded image as a Data URL
+    payload = {
+        "model": "gpt-4o-mini",
+        "messages": [
             {
                 "role": "user",
                 "content": [
                     {"type": "text", "text": text_input or "What's in this image?"},
                     {
                         "type": "image_url",
-                        "image_url": {"url": image_url},
-                    },
-                ],
+                        "image_url": {
+                            "url": f"data:image/jpeg;base64,{base64_image}"
+                        }
+                    }
+                ]
             }
         ],
-    )
+        "max_tokens": 300
+    }
 
-    # Extract and return the generated message
-    return completion.choices[0].message
+    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+    response_data = response.json()
 
+    # Handle errors
+    if response.status_code != 200:
+        raise ValueError(f"OpenAI API Error: {response_data.get('error', {}).get('message', 'Unknown Error')}")
+
+    # Extract and return the generated message
+    return response_data["choices"][0]["message"]
 
 css = """
   #output {
@@ -75,6 +82,6 @@ with gr.Blocks(css=css) as demo:
             generate_product_description, [input_img, text_input], [output_text]
         )
 
-# Set share=True to create a public URL
+# Launch Gradio app
 demo.queue(api_open=False)
-demo.launch(debug=True, share=True)
+demo.launch(debug=True)