app.py

# app.py
import torch
import gradio as gr
import os
import requests
import base64

# 假设 libra_eval 在你的 python 包 libra.eval 中
from libra.eval import libra_eval

model_path = "X-iZhang/libra-v1.0-7b"
image_files = ["./examples/curent.jpg", 
              "./examples/prior.jpg"]
prompt = "Provide a detailed description of the findings in the radiology image."
conv_mode = "libra_v1"

result = libra_eval(
    model_path=model_path,
    model_base=None,  
    image_file=image_files,
    query=prompt,
    temperature=0.9,
    top_p=0.8,
    max_new_tokens=512
)
print(result)

# def generate_radiology_description(
#     prompt: str,
#     uploaded_current: str,
#     uploaded_prior: str,
#     temperature: float,
#     top_p: float,
#     num_beams: int,
#     max_new_tokens: int
# ) -> str:
#     """
#     核心推理函数：
#     1. 仅通过用户上传的图片获取图像文件路径
#     2. 调用 libra_eval 来生成报告描述
#     3. 返回生成的结果或错误消息
#     """

#     # 确保用户上传了两张图片
#     if not uploaded_current or not uploaded_prior:
#         return "Please upload both current and prior images."

#     # 模型路径
#     model_path = "X-iZhang/libra-v1.0-7b"
#     conv_mode = "libra_v1"

#     try:
#         # 调用 libra_eval 进行推理
#         print("Before calling libra_eval")
#         output = libra_eval(
#             model_path=model_path,
#             model_base=None,  # 如果有必要，可指定基础模型
#             image_file=[uploaded_current, uploaded_prior],  # 两张本地图片路径
#             query=prompt,
#             temperature=temperature,
#             top_p=top_p,
#             num_beams=num_beams,
#             length_penalty=1.0,
#             num_return_sequences=1,
#             conv_mode=conv_mode,
#             max_new_tokens=max_new_tokens
#         )
#         print("After calling libra_eval, result:", output)
#         return output
#     except Exception as e:
#         return f"An error occurred: {str(e)}"

# # 构建 Gradio 界面
# with gr.Blocks() as demo:
#     # 标题和简单说明
#     gr.Markdown("# Libra Radiology Report Generator (Local Upload Only)")
#     gr.Markdown("Upload **Current** and **Prior** images below to generate a radiology description using the Libra model.")

#     # 用户输入：文本提示
#     prompt_input = gr.Textbox(
#         label="Prompt",
#         value="Describe the key findings in these two images."
#     )

#     # 上传本地图像（Current & Prior）
#     with gr.Row():
#         uploaded_current = gr.Image(
#             label="Upload Current Image",
#             type="filepath"
#         )
#         uploaded_prior = gr.Image(
#             label="Upload Prior Image",
#             type="filepath"
#         )

#     # 参数调节
#     with gr.Row():
#         temperature_slider = gr.Slider(
#             label="Temperature",
#             minimum=0.1,
#             maximum=1.0,
#             step=0.1,
#             value=0.7
#         )
#         top_p_slider = gr.Slider(
#             label="Top P",
#             minimum=0.1,
#             maximum=1.0,
#             step=0.1,
#             value=0.8
#         )
#         num_beams_slider = gr.Slider(
#             label="Number of Beams",
#             minimum=1,
#             maximum=20,
#             step=1,
#             value=2
#         )
#         max_tokens_slider = gr.Slider(
#             label="Max New Tokens",
#             minimum=10,
#             maximum=4096,
#             step=10,
#             value=128
#         )

#     # 用于显示模型生成的结果
#     output_text = gr.Textbox(
#         label="Generated Description",
#         lines=10
#     )

#     # 点击按钮时触发的推理逻辑
#     generate_button = gr.Button("Generate Description")
#     generate_button.click(
#         fn=generate_radiology_description,
#         inputs=[
#             prompt_input,
#             uploaded_current,
#             uploaded_prior,
#             temperature_slider,
#             top_p_slider,
#             num_beams_slider,
#             max_tokens_slider
#         ],
#         outputs=output_text
#     )

# if __name__ == "__main__":
#     demo.launch()