Update app.py

app.py

@@ -5,146 +5,122 @@ 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"
+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:
 
-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)
+    
+    if not uploaded_current or not uploaded_prior:
+        return "Please upload both current and prior images."
 
-# 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
-#     """
+    
+    model_path = "X-iZhang/libra-v1.0-7b"
+    conv_mode = "libra_v1"
 
-#     # 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."
+    try:
+        
+        print("Before calling libra_eval")
+        output = libra_eval(
+            model_path=model_path,
+            model_base=None,  
+            image_file=[uploaded_current, uploaded_prior],  
+            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)}"
 
-#     # 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)}"
+with gr.Blocks() as demo:
+    
+    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.")
 
-# # 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.")
+  
+    prompt_input = gr.Textbox(
+        label="Prompt",
+        value="Describe the key findings in these two images."
+    )
 
-#     # 用户输入：文本提示
-#     prompt_input = gr.Textbox(
-#         label="Prompt",
-#         value="Describe the key findings in these two images."
-#     )
+    
+    with gr.Row():
+        uploaded_current = gr.Image(
+            label="Upload Current Image",
+            type="filepath"
+        )
+        uploaded_prior = gr.Image(
+            label="Upload Prior Image",
+            type="filepath"
+        )
 
-#     # 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"
-#         )
+    
+    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
+        )
 
-#     # 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
-#     )
+    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()
+    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()