Update app.py

app.py

@@ -1,169 +1,47 @@
-import gradio as gr
-import os
+from transformers import AutoTokenizer, AutoModel
 
-os.system('pip install dashscope -U')
-import tempfile
-from pathlib import Path
-import secrets
-import dashscope
-from dashscope import MultiModalConversation, Generation
-from PIL import Image
+def get_dialogue_history(dialogue_history_list: list):
 
+    dialogue_history_tmp = []
+    for item in dialogue_history_list:
+        if item['role'] == 'counselor':
+            text = '咨询师：'+ item['content']
+        else:
+            text = '来访者：'+ item['content']
+        dialogue_history_tmp.append(text)
 
-# 设置API密钥
-YOUR_API_TOKEN = os.getenv('YOUR_API_TOKEN')
-dashscope.api_key = YOUR_API_TOKEN
-math_messages = []
-def process_image(image, shouldConvert=False):
-    # 获取上传文件的目录
-    global math_messages
-    math_messages = [] # reset when upload image
-    uploaded_file_dir = os.environ.get("GRADIO_TEMP_DIR") or str(
-        Path(tempfile.gettempdir()) / "gradio"
-    )
-    os.makedirs(uploaded_file_dir, exist_ok=True)
-    
-    # 创建临时文件路径
-    name = f"tmp{secrets.token_hex(20)}.jpg"
-    filename = os.path.join(uploaded_file_dir, name)
-    # 保存上传的图片
-    if shouldConvert:
-        new_img = Image.new('RGB', size=(image.width, image.height), color=(255, 255, 255))
-        new_img.paste(image, (0, 0), mask=image)
-        image = new_img
-    image.save(filename)
-    
-    # 调用qwen-vl-max-0809模型处理图片
-    messages = [{
-        'role': 'system',
-        'content': [{'text': 'You are a helpful assistant.'}]
-    }, {
-        'role': 'user',
-        'content': [
-            {'image': f'file://{filename}'},
-            {'text': 'Please describe the math-related content in this image, ensuring that any LaTeX formulas are correctly transcribed. Non-mathematical details do not need to be described.'}
-        ]
-    }]
-    
-    response = MultiModalConversation.call(model='qwen-vl-max-0809', messages=messages)
-    
-    # 清理临时文件
-    os.remove(filename)
-    
-    return response.output.choices[0]["message"]["content"]
+    dialogue_history = '\n'.join(dialogue_history_tmp)
 
-def get_math_response(image_description, user_question):
-    global math_messages
-    if not math_messages:
-        math_messages.append({'role': 'system', 'content': 'You are a helpful math assistant.'})
-    math_messages = math_messages[:1]
-    if image_description is not None:
-        content = f'Image description: {image_description}\n\n'
-    else:
-        content = ''
-    query = f"{content}User question: {user_question}"
-    math_messages.append({'role': 'user', 'content': query})
-    response = Generation.call(	
-        model="qwen2-math-72b-instruct",
-        messages=math_messages,	
-        result_format='message',
-        stream=True
-    )
-    answer = None
-    for resp in response:
-        if resp.output is None:
-            continue
-        answer = resp.output.choices[0].message.content
-        yield answer.replace("\\", "\\\\")
-    print(f'query: {query}\nanswer: {answer}')
-    if answer is None:
-        math_messages.pop()
-    else:
-        math_messages.append({'role': 'assistant', 'content': answer})
+    return dialogue_history + '\n' + '咨询师：'
 
+def get_instruction(dialogue_history):
+    instruction = f'''现在你扮演一位专业的心理咨询师，你具备丰富的心理学和心理健康知识。你擅长运用多种心理咨询技巧，例如认知行为疗法原则、动机访谈技巧和解决问题导向的短期疗法。以温暖亲切的语气，展现出共情和对来访者感受的深刻理解。以自然的方式与来访者进行对话，避免过长或过短的回应，确保回应流畅且类似人类的对话。提供深层次的指导和洞察，使用具体的心理概念和例子帮助来访者更深入地探索思想和感受。避免教导式的回应，更注重共情和尊重来访者的感受。根据来访者的反馈调整回应，确保回应贴合来访者的情境和需求。请为以下的对话生成一个回复。
 
-def math_chat_bot(image, sketchpad, question, state):
-    current_tab_index = state["tab_index"]
-    image_description = None
-    # Upload
-    if current_tab_index == 0:
-        if image is not None:
-            image_description = process_image(image)
-    # Sketch
-    elif current_tab_index == 1:
-        print(sketchpad)
-        if sketchpad and sketchpad["composite"]:
-            image_description = process_image(sketchpad["composite"], True)
-    yield from get_math_response(image_description, question)
+对话：
+{dialogue_history}'''
 
-css = """
-#qwen-md .katex-display { display: inline; }
-#qwen-md .katex-display>.katex { display: inline; }
-#qwen-md .katex-display>.katex>.katex-html { display: inline; }
-"""
+    return instruction
 
-def tabs_select(e: gr.SelectData, _state):
-    _state["tab_index"] = e.index
 
+tokenizer = AutoTokenizer.from_pretrained('qiuhuachuan/MeChat', trust_remote_code=True)
+model = AutoModel.from_pretrained('qiuhuachuan/MeChat', trust_remote_code=True).half().cuda()
+model = model.eval()
 
-# 创建Gradio接口
-with gr.Blocks(css=css) as demo:
-    gr.HTML("""\
-<p align="center"><img src="https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png" style="height: 60px"/><p>"""
-            """<center><font size=8>📖 Qwen2-Math Demo</center>"""
-            """\
-<center><font size=3>This WebUI is based on Qwen2-VL for OCR and Qwen2-Math for mathematical reasoning. You can input either images or texts of mathematical or arithmetic problems.</center>"""
-            )
-    state = gr.State({"tab_index": 0})
-    with gr.Row():
-        with gr.Column():
-            with gr.Tabs() as input_tabs:
-                with gr.Tab("Upload"):
-                    input_image = gr.Image(type="pil", label="Upload"),
-                with gr.Tab("Sketch"):
-                    input_sketchpad = gr.Sketchpad(type="pil", label="Sketch", layers=False)
-            input_tabs.select(fn=tabs_select, inputs=[state])
-            input_text = gr.Textbox(label="input your question")
-            with gr.Row():
-                with gr.Column():
-                    clear_btn = gr.ClearButton(
-                        [*input_image, input_sketchpad, input_text])
-                with gr.Column():
-                    submit_btn = gr.Button("Submit", variant="primary")
-        with gr.Column():
-            output_md = gr.Markdown(label="answer",
-                                    latex_delimiters=[{
-                                        "left": "\\(",
-                                        "right": "\\)",
-                                        "display": True
-                                    }, {
-                                        "left": "\\begin\{equation\}",
-                                        "right": "\\end\{equation\}",
-                                        "display": True
-                                    }, {
-                                        "left": "\\begin\{align\}",
-                                        "right": "\\end\{align\}",
-                                        "display": True
-                                    }, {
-                                        "left": "\\begin\{alignat\}",
-                                        "right": "\\end\{alignat\}",
-                                        "display": True
-                                    }, {
-                                        "left": "\\begin\{gather\}",
-                                        "right": "\\end\{gather\}",
-                                        "display": True
-                                    }, {
-                                        "left": "\\begin\{CD\}",
-                                        "right": "\\end\{CD\}",
-                                        "display": True
-                                    }, {
-                                        "left": "\\[",
-                                        "right": "\\]",
-                                        "display": True
-                                    }],
-                                    elem_id="qwen-md")
-        submit_btn.click(
-            fn=math_chat_bot,
-            inputs=[*input_image, input_sketchpad, input_text, state],
-            outputs=output_md)
-demo.launch()
+dialogue_history_list = []
+while True:
+    usr_msg = input('来访者：')
+    if usr_msg == '0':
+        exit()
+    else:
+        dialogue_history_list.append({
+            'role': 'client',
+            'content': usr_msg
+        })
+        dialogue_history = get_dialogue_history(dialogue_history_list=dialogue_history_list)
+        instruction = get_instruction(dialogue_history=dialogue_history)
+        response, history = model.chat(tokenizer, instruction, history=[], temperature=0.8, top_p=0.8)
+        print(f'咨询师：{response}')
+        dialogue_history_list.append({
+            'role': 'counselor',
+            'content': response
+        })