Update app.py

app.py

@@ -2,38 +2,38 @@ import gradio as gr
 import openai
 from openai import OpenAI
 import os
-import spaces
 import base64
 
 # Set API key and organization ID from environment variables
 api_key = os.environ.get("OPENAI_API_KEY")
 base_url = os.environ.get("OPENAI_API_BASE")
 client = OpenAI(api_key=api_key, base_url=base_url)
+sys_prompt = read('system_prompt.txt')
 
 # Define the model to be used
 MODEL = "gpt-4o"
 
-def process_text(text_input):
+def process_text(text_input,unit):
     if text_input:
         completion = client.chat.completions.create(
             model=MODEL,
             messages=[
-                {"role": "system", "content": "You are a helpful assistant. Help me with my math homework!"},
+                {"role": "system", "content": f" You are a helpful {unit} doctor." + sys_prompt},
                 {"role": "user", "content": f"Hello! Could you solve {text_input}?"}
             ]
         )
         return "Assistant: " + completion.choices[0].message.content
 
-def process_image(image_input):
+def process_image(image_input,unit):
     if image_input is not None:
         with open(image_input.name, "rb") as f:
             base64_image = base64.b64encode(f.read()).decode("utf-8")
         response = client.chat.completions.create(
             model=MODEL,
             messages=[
-                {"role": "system", "content": "You are a helpful assistant that responds in Markdown."},
+                {"role": "system", "content": f" You are a helpful {unit} doctor." + sys_prompt},
                 {"role": "user", "content": [
-                    {"type": "text", "text": "Help me understand what is it"},
+                    {"type": "text", "text": "Help me understand what this image"},
                     {"type": "image_url", "image_url": {
                         "url": f"data:image/png;base64,{base64_image}"}
                     }
@@ -43,11 +43,30 @@ def process_image(image_input):
         )
         return response.choices[0].message.content
 
-def main(text_input="", image_input=None):
+def main(text_input="", image_input=None, unit=""):
     if text_input and image_input is None:
-        return process_text(text_input)
+        return process_text(text_input,unit)
     elif image_input is not None:
-        return process_image(image_input)
+        return process_image(image_input,unit)
 
-iface = gr.Interface(fn=main, inputs=["text", gr.Image()], outputs="text")
-iface.launch()
+with gr.Blocks() as iface:
+    with gr.Accordion(""):
+        gr.Markdown("""医学报告助手 GPT-4o""")
+        unit = gr.Dropdown(label="🩺科室", value='内科', elem_id="units",
+                            choices=["内科", "外科", "妇产科", "男科", "儿科", \
+                                     "五官科", "肿瘤科", "皮肤性病科", "中医科", "传染科", "精神心理科", \
+                                        "整形美容科", "营养科", "生殖中心", "麻醉医学科", "医学影像科", \
+                                            "急诊科", "检验科"])
+    with gr.Row():
+        output_box = gr.Textbox(label="Output")  # Create an output textbox
+    with gr.Row():
+        image_btn = gr.Image(type="filepath", label="上传图片")  # Create an image upload button
+        text_input = gr.Textbox(label="输入")  # Create a text input box
+    with gr.Row():
+        submit_btn = gr.Button("🚀 确认")  # Create a submit button
+        clear_btn = gr.ClearButton(output_box, value="🗑️ 清空") # Create a clear button
+
+    # Set up the event listeners
+    submit_btn.click(main, inputs=[text_input, image_btn, unit], outputs=output_box)
+
+iface.launch()  # Launch the Gradio interface