Create app.py

app.py

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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=(800, 800), 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 img_base64.decode('utf-8')
+
+api_key = os.getenv('API_KEY')
+
+def call_deplot_api(image_path, content, temperature=0.2, top_p=0.7, max_tokens=1024):
+    image_base64 = filepath_to_base64(image_path)
+    invoke_url = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/3bc390c7-eeec-40f7-a64d-0c6a719985f7"
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Accept": "application/json",
+    }
+
+    payload = {
+        "messages": [
+            {
+                "content": f"{content} <img src=\"data:image/jpeg;base64,{image_base64}\" />",
+                "role": "user"
+            }
+        ],
+        "temperature": temperature,
+        "top_p": top_p,
+        "max_tokens": max_tokens,
+        "stream": False
+    }
+
+    session = requests.Session()
+    response = session.post(invoke_url, headers=headers, json=payload)
+
+    while response.status_code == 202:
+        request_id = response.headers.get("NVCF-REQID")
+        fetch_url = f"https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/{request_id}"
+        response = session.get(fetch_url, headers=headers)
+
+    response.raise_for_status()
+    response_body = response.json()
+    print(response_body)
+
+    # Processar a resposta conforme necessário
+    # Este exemplo apenas imprime a resposta, mas você pode adaptá-lo conforme necessário
+    return response_body
+
+# Configuração da Interface Gráfica
+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=1024, step=1, value=1024, label="Max Tokens")
+
+iface = gr.Interface(fn=call_deplot_api,
+                     inputs=[image_input, content_input, temperature_input, top_p_input, max_tokens_input],
+                     outputs="text",
+                     title="Google DePlot API Explorer",
+                     description="""
+<div style="text-align: center; font-size: 1.5em; margin-bottom: 20px;">
+<strong>Explore Visual Language Understanding with Google DePlot</strong>
+</div>
+<p>
+    Utilize Google DePlot to translate images of plots or charts into linearized tables. This one-shot visual language understanding solution offers a unique approach to interpreting visual data.
+</p>
+...
+"""
+                    )
+
+iface.launch()