How to implement GPT or LLM, AI chat interface

[monkeycc]

How to implement GPT or LLM, AI chat interface
....

chatbot
Input box
button

Chatbot

Human issues:
Robot answer:

Human issues:
Robot answer:

I'm not sure how to create such a chat interface

[p2hpi2n31fophp]

add_input_text can do simple interface. but rich text need to do more work .
if use local model use ollama is the simple way.

def fetch_chat_stream_result(model_name="",global_context_json=""):
    data_req={"model":model_name,"messages":global_context_json,"stream": True}    
    request = urllib.request.Request(urllib.parse.urljoin(api_url, "/api/chat"),data=json.dumps(data_req).encode("utf-8"),headers={"Content-Type": "application/json"},method="POST")
    with urllib.request.urlopen(request) as resp:
        for line in resp:
            data = json.loads(line.decode("utf-8"))
            if "message" in data:
                time.sleep(INTERVAL_SECONDS_a)
                yield data["message"]["content"]
def generate_multigraph_fixed_ai_response(ipt_tag="",reply_tag="",global_context_json="",table_parent="",q_tag="",chat_context_json_path="",flowing_flag=""):
    global new_complete_message, is_output_paused
    try:
        model_name=dpg.get_value(ipt_tag)
        ai_message="{}回答:::".format(model_name)
        count=0
        for one_word in fetch_chat_stream_result(model_name=model_name,global_context_json=global_context_json):
            # 添加暂停检查
            while is_output_paused:
                time.sleep(0.1)
                continue
                
            replay_content=dpg.get_value(reply_tag)
            if count%70==0:
                dpg.configure_item(reply_tag,default_value=replay_content+"\n"+one_word)
            else:
                dpg.configure_item(reply_tag,default_value=replay_content+one_word)
            ai_message += one_word
            count+=1
            
        global_context_json.append({"role": "assistant", "content": ai_message})
        replay_content=dpg.get_value(reply_tag)    
        new_complete_message=replay_content
        think_chain,reply_content=extract_think_content(input_string=new_complete_message)
        one_response_to_current_table(table_parent=table_parent,q_tag=q_tag,think_chain=think_chain,new_complete_message=reply_content,chat_context_json_path=chat_context_json_path)
        dpg.configure_item(reply_tag,default_value=replay_content+"\n\n")
    except Exception as e:
        print(e,11111111)
    finally:
        pass

if use api key check the api document from model provider.

def stream_chat_response_APICALL_FROM_MODEL_PROVIDER(q_tag="",messages="", reply_tag="", table_tag="",tag_ipt_apikey="",json_path=""):
    global is_output_paused
    value_api_key=dpg.get_value(tag_ipt_apikey)
    client_object_from_AIreq = OpenAI(api_key=value_api_key, base_url=base_url)
    full_response=""
    if model_api_ds=="deepseek-reasoner":
        print(messages,"++++++++++++",model_api_ds)
        try:
            response = client_object_from_AIreq.chat.completions.create(
                model=model_api_ds,
                messages=messages,
                stream=True
            )
            reasoning_content = ""
            content = ""
            for chunk in response:
                while is_output_paused:
                    time.sleep(0.1)
                    continue
                # 检查delta中的content
                if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content:
                    content_reply = chunk.choices[0].delta.content
                    content += content_reply
                    # current_text = dpg.get_value(reply_tag)
                    # dpg.configure_item(reply_tag, default_value=current_text + content)
                
                # 如果有reasoning_content，也处理它
                if hasattr(chunk.choices[0].delta, 'reasoning_content') and chunk.choices[0].delta.reasoning_content:
                    reasoning = chunk.choices[0].delta.reasoning_content
                    reasoning_content+=reasoning
                    # full_response += f"\n推理过程：{reasoning}\n"
                    # current_text = dpg.get_value(reply_tag)
                    dpg.configure_item(reply_tag, default_value=reasoning_content+content)

            full_response=reasoning_content + content
            try:
                if reasoning_content and content:
                    one_response_to_current_table(table_parent=table_tag,q_tag=q_tag,think_chain=reasoning_content,new_complete_message=content,chat_context_json_path=json_path)
            except Exception as e:
                print (e)
                assert 1>2
            # return full_response
        except Exception as e:
            print(e)
            error_msg = f"API调用错误: {str(e)}"
            # dpg.configure_item(reply_tag, default_value=error_msg)
            # is_output_paused=
            print(error_msg)
            return error_msg

Everytime commit a input message start a thread to configure add_input_text or other widget of text showing.