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| import gradio as gr | |
| import requests | |
| import os | |
| import base64 | |
| from PIL import Image | |
| import io | |
| import json | |
| def resize_image(image_path, max_size=(512, 512), quality=85): | |
| with Image.open(image_path) as img: | |
| img.thumbnail(max_size, Image.Resampling.LANCZOS) | |
| buffer = io.BytesIO() | |
| img.save(buffer, format="JPEG", quality=quality) | |
| return buffer.getvalue() | |
| def filepath_to_base64(image_path): | |
| img_bytes = resize_image(image_path) | |
| img_base64 = base64.b64encode(img_bytes) | |
| return f"data:image/jpeg;base64,{img_base64.decode('utf-8')}" | |
| api_key = os.getenv('API_KEY') | |
| def call_neva_22b_api(image_path, content, temperature=0.2, top_p=0.7, max_tokens=512, quality=6, humor=0, creativity=6, helpfulness=6): | |
| print(f"Caminho da imagem recebida: {image_path}") | |
| print(f"Conteúdo: {content}") | |
| # Imprime os novos parâmetros | |
| print(f"Quality: {quality}, Humor: {humor}, Creativity: {creativity}, Helpfulness: {helpfulness}") | |
| image_base64 = filepath_to_base64(image_path) | |
| invoke_url = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/8bf70738-59b9-4e5f-bc87-7ab4203be7a0" | |
| headers = { | |
| "Authorization": f"Bearer {api_key}", | |
| "accept": "text/event-stream", | |
| "content-type": "application/json", | |
| } | |
| payload = { | |
| "messages": [ | |
| { | |
| "content": f"{content} <img src=\"{image_base64}\" />", | |
| "role": "user" | |
| }, | |
| { | |
| "labels": { | |
| "creativity": creativity, | |
| "helpfulness": helpfulness, | |
| "humor": humor, | |
| "quality": quality | |
| }, | |
| "role": "assistant" | |
| } | |
| ], | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "max_tokens": max_tokens, | |
| "stream": True | |
| } | |
| response = requests.post(invoke_url, headers=headers, json=payload, stream=True) | |
| if response.status_code != 200: | |
| print(f"Erro na requisição: {response.status_code}") | |
| try: | |
| error_details = response.json() | |
| print(error_details) | |
| except ValueError: | |
| print(response.text) | |
| else: | |
| response_text = "" | |
| for line in response.iter_lines(): | |
| if line: | |
| try: | |
| # Decode the line from bytes to string | |
| decoded_line = line.decode('utf-8') | |
| # Remove the "data: " prefix | |
| if decoded_line.startswith('data: '): | |
| json_str = decoded_line[6:] # Remove the first 6 characters ('data: ') | |
| json_line = json.loads(json_str) | |
| # Assuming the structure is consistent with the examples you provided. | |
| content_parts = json_line.get("choices", [{}])[0].get("delta", {}).get("content", "") | |
| response_text += content_parts | |
| else: | |
| print(f"Unexpected line format: {decoded_line}") | |
| except json.JSONDecodeError as e: | |
| print(f"Error decoding JSON from response line: {e}") | |
| print(f"Faulty line: {line}") | |
| return response_text | |
| content_input = gr.Textbox(lines=2, placeholder="Enter your content here...", label="Content") | |
| image_input = gr.Image(type="filepath", label="Upload Image") | |
| temperature_input = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.2, label="Temperature") | |
| top_p_input = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label="Top P") | |
| max_tokens_input = gr.Slider(minimum=1, maximum=512, step=1, value=512, label="Max Tokens") | |
| quality_input = gr.Slider(minimum=0, maximum=9, step=1, value=6, label="Quality") | |
| humor_input = gr.Slider(minimum=0, maximum=9, step=1, value=0, label="Humor") | |
| creativity_input = gr.Slider(minimum=0, maximum=9, step=1, value=6, label="Creativity") | |
| helpfulness_input = gr.Slider(minimum=0, maximum=9, step=1, value=6, label="Helpfulness") | |
| iface = gr.Interface(fn=call_neva_22b_api, | |
| inputs=[image_input, content_input, temperature_input, top_p_input, max_tokens_input, quality_input, humor_input, creativity_input, helpfulness_input], | |
| outputs="text", | |
| title="NEVA 22B DEMO", | |
| description=""" | |
| <div style="text-align: center; font-size: 1.5em; margin-bottom: 20px;"> | |
| <strong>Unlock the Power of AI with NeVA-22B Vision-Language Model</strong> | |
| </div> | |
| <p> | |
| Dive into the next generation of AI with NeVA-22B, an advanced multi-modal vision-language model that redefines the boundaries of technology. Developed with a 22 billion parameter architecture, NeVA-22B excels in understanding and generating responses that incorporate both text and images, offering a groundbreaking platform for multi-modal AI exploration. | |
| </p> | |
| <p> | |
| <strong>How to Use:</strong> | |
| </p> | |
| <ol> | |
| <li>Upload an <strong>image</strong> to provide visual context.</li> | |
| <li>Enter your <strong>content</strong> in the textbox to pose a question or prompt.</li> | |
| <li>Utilize the <strong>Temperature</strong> and <strong>Top P</strong> sliders to adjust the creativity and diversity of the responses.</li> | |
| <li>Choose the <strong>Max Tokens</strong> to control the response length.</li> | |
| <li>Modify <strong>Quality</strong>, <strong>Humor</strong>, <strong>Creativity</strong>, and <strong>Helpfulness</strong> sliders to fine-tune the model's output according to your needs.</li> | |
| <li>Hit <strong>Submit</strong> to experience the model's capability to generate insightful responses based on your textual and visual inputs.</li> | |
| </ol> | |
| <p> | |
| <strong>Empowered by NVIDIA's cutting-edge AI technologies, NeVA-22B API Explorer opens up new avenues for engaging with multi-modal AI, accessible to everyone at no cost.</strong> | |
| </p> | |
| <p> | |
| <strong>HF Created by:</strong> @artificialguybr (<a href="https://twitter.com/artificialguybr">Twitter</a>) | |
| </p> | |
| <p> | |
| <strong>Explore further:</strong> <a href="https://artificialguy.com">artificialguy.com</a> | |
| </p> | |
| """ | |
| ) | |
| iface.launch() |