app.py

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")
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")
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")

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()