Update app.py

app.py

@@ -1,51 +1,14 @@
 import gradio as gr
+import os
 import requests
 import base64
-import os
-
-# argparse 的简单替代，如有需要可替换为 argparse
-from argparse import Namespace
 
 # 假设 libra_eval 在你的 python 包 libra.eval 中
 from libra.eval import libra_eval
 
-# 预定义图像及其链接（或者本地路径）
-DEFAULT_IMAGES = {
-    "Image 1": "./examples/curent.jpg",
-    "Image 2": "./examples/prior.jpg"
-}
-
-def image_url_to_base64(image_url: str) -> str:
-    """
-    将远程图片 URL 转换为 Base64 数据 URI。
-    如果请求失败，则返回提示文本。
-    """
-    try:
-        response = requests.get(image_url)
-        response.raise_for_status()
-        base64_image = base64.b64encode(response.content).decode("utf-8")
-        return f"data:image/jpeg;base64,{base64_image}"
-    except Exception as e:
-        return f"<p style='color: red;'>Failed to load image: {e}</p>"
-
-def generate_image_html(image_url: str) -> str:
-    """
-    生成一个 <img> 标签的 HTML，用于在 Gradio 中以预览形式显示图片。
-    如果是 http(s) 链接，则尝试转换为 Base64；如果是本地路径，直接使用 file://。
-    """
-    # 判断是否以 http(s) 开头
-    if image_url.startswith("http"):
-        base64_image = image_url_to_base64(image_url)
-        return f'<img src="{base64_image}" style="width: 200px; height: auto; display: inline-block; margin: 10px; border-radius: 10px;" />'
-    else:
-        # 直接使用本地路径
-        return f'<img src="file://{image_url}" style="width: 200px; height: auto; display: inline-block; margin: 10px; border-radius: 10px;" />'
-
 def generate_radiology_description(
     prompt: str,
-    selected_current: str,
     uploaded_current: str,
-    selected_prior: str,
     uploaded_prior: str,
     temperature: float,
     top_p: float,
@@ -54,28 +17,25 @@ def generate_radiology_description(
 ) -> str:
     """
     核心推理函数：
-    1. 获取用户输入或默认图片
+    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
     """
-    # 如果用户上传了图片，则优先使用上传的图片；否则使用默认图片
-    current_image = uploaded_current if uploaded_current else DEFAULT_IMAGES.get(selected_current)
-    prior_image = uploaded_prior if uploaded_prior else DEFAULT_IMAGES.get(selected_prior)
 
-    # 确保用户选择或上传了两张图片
-    if not current_image or not prior_image:
-        return "Please select or upload both current and prior images."
+    # 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（示例）
-    model_path = "/nfs/LLaVA-ai4bio/gla-biomed-playground/final_model/finetuned_model/llava-libra-test"
+    # 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
         output = libra_eval(
             model_path=model_path,
-            model_base=None,
-            image_file=[current_image, prior_image],
+            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,
@@ -89,84 +49,57 @@ def generate_radiology_description(
     except Exception as e:
         return f"An error occurred: {str(e)}"
 
-# 在 Gradio 中构建 UI
-# Blocks 为最新的容器API，可以更好地对布局进行控制
+# 构建 Gradio 界面
 with gr.Blocks() as demo:
     # 标题和简单说明
-    gr.Markdown("# Libra Radiology Report Generator")
-    gr.Markdown("Use **Libra** to generate radiology image descriptions. Provide a **Current** and a **Prior** image below.")
+    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.")
 
-    # 用户输入的文本
-    with gr.Row():
-        prompt_input = gr.Textbox(
-            label="Prompt", 
-            value="Provide a detailed description of the findings in the radiology image."
-        )
+    # 用户输入：文本提示
+    prompt_input = gr.Textbox(
+        label="Prompt",
+        value="Describe the key findings in these two images."
+    )
 
-    # 当前图像（Current Image）和历史对比图像（Prior Image）
+    # 上传本地图像（Current & Prior）
     with gr.Row():
-        with gr.Column():
-            gr.Markdown("### Current Image")
-            # 预览默认图像
-            for img in DEFAULT_IMAGES.values():
-                gr.HTML(generate_image_html(img))
-            # 在Radio中选择
-            selected_current = gr.Radio(
-                label="Select Current Image", 
-                choices=list(DEFAULT_IMAGES.keys()), 
-                value="Image 1"
-            )
-            # 或者上传一张新的
-            uploaded_current = gr.Image(
-                label="Or Upload Current Image", 
-                type="filepath", 
-                tool="editor"
-            )
-
-        with gr.Column():
-            gr.Markdown("### Prior Image")
-            # 同样显示默认图像
-            for img in DEFAULT_IMAGES.values():
-                gr.HTML(generate_image_html(img))
-            selected_prior = gr.Radio(
-                label="Select Prior Image", 
-                choices=list(DEFAULT_IMAGES.keys()), 
-                value="Image 2"
-            )
-            uploaded_prior = gr.Image(
-                label="Or Upload Prior Image", 
-                type="filepath", 
-                tool="editor"
-            )
+        uploaded_current = gr.Image(
+            label="Upload Current Image",
+            type="filepath"
+        )
+        uploaded_prior = gr.Image(
+            label="Upload Prior Image",
+            type="filepath"
+        )
 
-    # 一些可调参数
+    # 参数调节
     with gr.Row():
         temperature_slider = gr.Slider(
-            label="Temperature", 
-            minimum=0.1, 
-            maximum=1.0, 
-            step=0.1, 
+            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, 
+            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, 
+            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, 
+            label="Max New Tokens",
+            minimum=10,
+            maximum=4096,
+            step=10,
             value=128
         )
 
@@ -181,19 +114,18 @@ with gr.Blocks() as demo:
     generate_button.click(
         fn=generate_radiology_description,
         inputs=[
-            prompt_input, 
-            selected_current, 
+            prompt_input,
             uploaded_current,
-            selected_prior, 
             uploaded_prior,
-            temperature_slider, 
-            top_p_slider, 
-            num_beams_slider, 
+            temperature_slider,
+            top_p_slider,
+            num_beams_slider,
             max_tokens_slider
         ],
         outputs=output_text
     )
 
-# 启动 Gradio 应用（将 share 设置为 True 以便在 Hugging Face Spaces 中分享）
 if __name__ == "__main__":
+    # 启动 Gradio 应用
+    # 将 share 设置为 True 以便在 Hugging Face Spaces 中分享
     demo.launch(share=True)