File size: 2,549 Bytes
1db7196 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import gradio as gr
from openai import OpenAI
import base64
import io
# Initialize the client pointing to your vLLM server
client = OpenAI(
base_url="http://172.16.34.29:8006/v1",
api_key="vllm-token",
)
def encode_image_to_base64(image):
"""Converts PIL image to raw base64 string (no data-uri prefix)."""
if image is None:
return None
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
def run_translation(source_code, target_code, text_input, image_input):
# Construct the base dictionary
# The schema requires all these keys to be present in the mapping
payload = {
"source_lang_code": source_code,
"target_lang_code": target_code,
"text": None,
"image": None
}
if image_input is not None:
payload["type"] = "image"
payload["image"] = encode_image_to_base64(image_input)
else:
if not text_input.strip():
return "Please provide text or an image."
payload["type"] = "text"
payload["text"] = text_input
try:
# Crucial: We pass the payload as the single item in the content list
response = client.chat.completions.create(
model="translate_gemma",
messages=[{
"role": "user",
"content": [payload] # vLLM expects exactly [ { ... } ]
}],
max_tokens=500
)
return response.choices[0].message.content
except Exception as e:
return f"⚠️ Error: {str(e)}"
# --- Gradio UI Layout ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🌍 TranslateGemma 27B")
gr.Markdown("Corrected schema for vLLM inference.")
with gr.Row():
src_code = gr.Textbox(label="Source Language Code", value="en")
tgt_code = gr.Textbox(label="Target Language Code", value="bn")
with gr.Row():
with gr.Column():
text_box = gr.Textbox(label="Text Input", placeholder="Type English here...", lines=5)
image_box = gr.Image(label="Image Input", type="pil")
submit_btn = gr.Button("Translate", variant="primary")
with gr.Column():
output_box = gr.Textbox(label="Bangla Translation", interactive=False, lines=10)
submit_btn.click(
fn=run_translation,
inputs=[src_code, tgt_code, text_box, image_box],
outputs=output_box
)
if __name__ == "__main__":
demo.launch(share=True) |