Create app.py

app.py

@@ -0,0 +1,107 @@
+import os
+import base64
+import gradio as gr
+from gradio_client import Client
+
+MODEL_NAME = "QWEN"
+client_chat = os.environ.get("CHAT_URL")
+client_vl = os.environ.get("VL_URL")
+
+def read(filename):
+    with open(filename) as f:
+        data = f.read()
+    return data
+    
+SYS_PROMPT = read('system_prompt.txt')
+
+
+DESCRIPTION = '''
+<div>
+<h1 style="text-align: center;">家庭医生demo</h1>
+<p>🩺一个帮助您分析症状和检验报告的家庭医生(AI诊疗助手)。</p>
+<p>🔎 选择您需要咨询的科室医生，在输入框中输入症状描述或者体检信息等；您也可以在图片框中上传检测报告图。</p>
+<p>🦕 请注意生成信息可能不准确，且不具备任何实际参考价值，如有需要请联系专业医生。</p>
+</div>
+'''
+
+
+css = """
+h1 {
+    text-align: center;
+    display: block;
+}
+footer {
+    display:none !important
+}
+"""
+
+
+LICENSE = '采用 ' + MODEL_NAME + ' 模型'
+
+
+
+
+def process_text(text_input, unit):
+    client = Client(chat_url)
+    response = client.predict(
+        query=text_input,
+        history=[],
+        system = f" You are a experienced {unit} doctor AI assistant." + SYS_PROMPT,
+        api_name="/model_chat"
+    )
+    return response
+ 
+
+def encode_image_to_base64(image_input):
+    buffered = io.BytesIO()
+    image_input.save(buffered, format="JPEG")
+    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    return img_str
+
+def process_image(image_input, unit):
+    if image_input is not None:
+        print(image_input)
+        #with open(image_input.name, "rb") as f:
+        #    base64_image = base64.b64encode(f.read()).decode("utf-8")
+        client = Client(client_vl)
+        # base64_image = encode_image_to_base64(image_input)
+        prompt = f" You are a experienced {unit} doctor AI assistant." + SYS_PROMPT + "Help me understand what is in this picture and analysis."
+        response = client.predict(
+            prompt, 
+            image_input, 
+            fn_index=5
+        )
+        return response     
+
+
+def main(text_input="", image_input=None, unit=""):
+    if text_input and image_input is None:
+        return process_text(text_input,unit)
+    elif image_input is not None:
+        return process_image(image_input,unit)
+
+with gr.Blocks(theme='shivi/calm_seafoam', css=css, title="家庭医生AI助手") as iface:
+    with gr.Accordion(""):
+        gr.Markdown(DESCRIPTION)
+        unit = gr.Dropdown(label="🩺科室", value='中医科', elem_id="units",
+                            choices=["中医科", "内科", "外科", "妇产科",  "儿科", \
+                                     "五官科", "男科", "皮肤性病科", "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", \
+                                            "骨科", "肿瘤科", "急诊科", "检验科"])
+    with gr.Row():
+        output_box = gr.Markdown(label="分析")  # Create an output textbox
+    with gr.Row():
+        image_input = gr.Image(type="pil", 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,image_input,text_input], value="🗑️ 清空") # Create a clear button
+
+    # Set up the event listeners
+    submit_btn.click(main, inputs=[text_input, image_input, unit], outputs=output_box)
+
+    gr.Markdown(LICENSE)
+    
+#gr.close_all()
+
+iface.queue().launch(show_api=False)  # Launch the Gradio interface