Create app.py

app.py

@@ -0,0 +1,78 @@
+import gradio as gr
+import requests
+import os
+import base64
+from PIL import Image
+import io
+
+api_key = os.getenv('API_KEY')
+
+
+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')
+
+def format_response(response_body):
+    content = response_body['choices'][0]['message']['content']
+    formatted_content = content.replace("<0x0A>", "\n")
+    return formatted_content
+
+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/0bcd1a8c-451f-4b12-b7f0-64b4781190d1"
+    api_key = os.getenv('API_KEY')
+    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()
+    return format_response(response_body)
+
+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="Kosmos-2 API Explorer",
+                     description="""
+<div style="text-align: center; font-size: 1.5em; margin-bottom: 20px;">
+<strong>Explore Visual Language Understanding with Kosmos-2</strong>
+</div>
+<p>
+    Kosmos-2 model is a groundbreaking multimodal large language model (MLLM). Kosmos-2 is designed to ground text to the visual world, enabling it to understand and reason about visual elements in images.
+</p>
+"""
+                    )
+
+iface.launch()