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| import gradio as gr | |
| import openai | |
| import os | |
| import requests | |
| import base64 | |
| from PIL import Image | |
| import io | |
| openai_key = os.getenv('OpenAIKey') | |
| getimg_key = os.getenv('GetImgAIKey') | |
| gpt_model = "gpt-4o-mini" | |
| img_endpoint = "stable-diffusion-xl" | |
| img_model = "juggernaut-xl-v10" | |
| step_count = 14 | |
| img_height = 512 | |
| img_width = 512 | |
| # Helper functions | |
| def get_openai_response(api_key, model, prompt): | |
| client = openai.OpenAI(api_key=api_key) | |
| response = client.ChatCompletion.create( | |
| model=model, | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| ) | |
| return response.choices[0].message['content'] | |
| def generate_response(prompt): | |
| response = get_openai_response(api_key=openai_key, model=gpt_model, prompt=prompt) | |
| return response | |
| def generate_image(prompt): | |
| url = f"https://api.getimg.ai/v1/{img_endpoint}/text-to-image" | |
| headers = { | |
| "authorization": f"Bearer {getimg_key}", | |
| "accept": "application/json", | |
| "content-type": "application/json" | |
| } | |
| payload = { | |
| "prompt": prompt, | |
| "model": img_model, | |
| "width": img_width, | |
| "height": img_height, | |
| "steps": step_count, | |
| "output_format": "jpeg", | |
| "response_format": "b64" | |
| } | |
| response = requests.post(url, headers=headers, json=payload) | |
| if response.status_code == 200: | |
| # Get the base64 encoded image from the response | |
| image_base64 = response.json().get('image') | |
| if image_base64: | |
| # Decode the base64 image and convert it to an image format Gradio can use | |
| image_data = base64.b64decode(image_base64) | |
| image = Image.open(io.BytesIO(image_data)) | |
| return image | |
| else: | |
| return "Error: Image generation failed, no image data returned." | |
| else: | |
| return f"Error: {response.status_code} - {response.text}" | |
| # Gradio Blocks | |
| with gr.Blocks(css="styles.css", theme='TMBoeren/ThomasMore') as demo: | |
| gr.Markdown("<h1 class='gr-title'>Get ChatGPT response</h1>") | |
| gr.Markdown("<span class='gr-description'>Enter your question to get responses from ChatGPT</span>") | |
| input_box = gr.Textbox(label="Your Question", lines=2, elem_id="input_textbox") | |
| submit_btn = gr.Button(value="Submit") | |
| output_box = gr.Textbox(label="Response", lines=5, elem_id="output_textbox") | |
| submit_btn.click(generate_response, inputs=[input_box], outputs=[output_box]) | |
| gr.Markdown("<h1 class='gr-title'>Generate image</h1>") | |
| gr.Markdown("<span class='gr-description'>Enter your prompt to generate an image</span>") | |
| image_prompt_textbox = gr.Textbox(label="Your prompt", lines=2, elem_id="image_prompt_textbox") | |
| image_submit_btn = gr.Button(value="Submit") | |
| image_output = gr.Image(label="Generated Image", elem_id="image_output", width=img_width, height=img_height) | |
| image_submit_btn.click(generate_image, inputs=[image_prompt_textbox], outputs=[image_output]) | |
| demo.launch(show_api=False, allowed_paths=["fonts"]) | |