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