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import gradio as gr
import requests
import json
import base64
def call_api(image, text_input):
# Check if image is provided
if image is None:
return "请上传图片"
# Handle image processing
try:
# Read image file
if isinstance(image, str):
# If it's a filepath
with open(image, "rb") as f:
image_data = f.read()
else:
# If it's already in memory (Gradio might pass different types)
image_data = image
# Convert to base64
image_base64 = base64.b64encode(image_data).decode("utf-8")
image_url = f"data:image/jpeg;base64,{image_base64}"
# Construct request payload
payload = {
"model": "/data1/models/PKUAgri/qwen2_vl_lora_sft",
"messages": [
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": image_url}},
{"type": "text", "text": text_input}
]
}
]
}
headers = {
"Content-Type": "application/json"
}
# Make API call
response = requests.post(
"http://10.1.10.115:4001/v1/chat/completions",
data=json.dumps(payload),
headers=headers,
timeout=30
)
if response.status_code == 200:
res = response.json()
return res["choices"][0]["message"]["content"]
else:
return f"错误: {response.status_code} - {response.text}"
except Exception as e:
return f"处理请求时出错: {str(e)}"
# Build Gradio interface
with gr.Blocks() as demo:
gr.Markdown("## 🌄 病虫害识别模型")
with gr.Row():
image_input = gr.Image(type="filepath", label="上传图片")
text_input = gr.Textbox(label="请输入你的问题", placeholder="例如:图中有什么?")
submit_btn = gr.Button("提交")
output = gr.Textbox(label="回答", lines=5)
submit_btn.click(fn=call_api, inputs=[image_input, text_input], outputs=output)
# Launch the app
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port= 40011, share=True) |