Update app.py

app.py

@@ -8,126 +8,143 @@ import base64
 # 假设 libra_eval 在你的 python 包 libra.eval 中
 from libra.eval import libra_eval
 
-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"
+image_files = ["./examples/curent.jpg", 
+              "./examples/prior.jpg"]
+prompt = "Provide a detailed description of the findings in the radiology image."
+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."
+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)
 
-    # 模型路径
-    model_path = "X-iZhang/libra-v1.0-7b"
-    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:
+#     """
+#     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
+#     """
 
-    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)}"
+#     # 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."
 
-# 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.")
+#     # 模型路径
+#     model_path = "X-iZhang/libra-v1.0-7b"
+#     conv_mode = "libra_v1"
 
-    # 用户输入：文本提示
-    prompt_input = gr.Textbox(
-        label="Prompt",
-        value="Describe the key findings in these two images."
-    )
+#     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)}"
 
-    # 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"
-        )
+# # 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.")
 
-    # 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
-        )
+#     # 用户输入：文本提示
+#     prompt_input = gr.Textbox(
+#         label="Prompt",
+#         value="Describe the key findings in these two images."
+#     )
 
-    # 用于显示模型生成的结果
-    output_text = gr.Textbox(
-        label="Generated Description",
-        lines=10
-    )
+#     # 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"
+#         )
 
-    # 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
-    )
+#     # 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
+#         )
 
-if __name__ == "__main__":
-    demo.launch()
+#     # 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()