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import matplotlib.pyplot as plt
import streamlit as st
import seaborn as sns
from interact_with_llm import Agent
import json
import numpy as np
agent = Agent()
QUESTION_ANSWER_MAP = {
"Question1: Are you usually?": {
"A 'Good Mixer with groups of people": "Extrovert",
"Rather quiet and reserved": "Introvert"
},
"Question2: Among your friends, you are?": {
"Full of news about everybody": "Extrovert",
"One of the last to hear what is going on": "Introvert"
},
"Question3: In doing something that many other people do, you would rather?": {
"Invent a way of your own": "Intuition",
"Do it in the accepted way": "Sensing"
},
"Question4: Do you admire the people who are?": {
"Normal-acting to never make themselves the center of attention": "Sensing",
"Too original and individual to care whether they are the center of attention or not": "Intuition"
},
"Question5: Do you more often let?": {
"Your heart rule your head": "Feeling",
"Your head rule your heart": "Thinking"
},
"Question6: Do you usually?": {
"Value emotion more than logic": "Feeling",
"Value logic more than feelings": "Thinking"
},
"Question7: When you go somewhere for the day, you would rather": {
"Plan what you will do and when": "Judging",
"Just go": "Perceiving"
},
"Question8: When you have a special job to do, you like to": {
"Organize it carefully before you start": "Judging",
"Find out what is necessary as you go along": "Perceiving"
}
}
final = {"E_I": 0, "S_N":0, "T_F":0, "J_P":0 }
personality_map = {"Extrovert":("E_I", 0), "Introvert":("E_I", 50),
"Sensing":("S_N", 0), "Intuition":("S_N", 50),
"Thinking":("T_F", 0), "Feeling":("T_F", 50),
"Judging":("J_P", 0), "Perceiving":("J_P", 50)}
def process_choice(dic):
final = {"J_P":0 ,"T_F":0, "S_N":0, "E_I": 0 }
for k, v in dic.items():
pair = personality_map[QUESTION_ANSWER_MAP[k][v]]
final[pair[0]] += pair[1]
return final
def process_other(other):
agent = Agent()
result = {}
for k,v in other.items():
pair = agent.send_msg({k:v})
result[pair[0]] = pair[1]
final = {"J_P":0 ,"T_F":0, "S_N":0, "E_I": 0 }
for k,v in result.items():
pair = personality_map[k]
try:
final[pair[0]] += 0.5 * int(v)
except:
final[pair[0]] += 25
return final
def analyze_post(post):
agent = Agent()
result = agent.send_post(post)
print(result)
try:
data = json.loads(result)
final = {"J_P":0 ,"T_F":0, "S_N":0, "E_I": 0 }
final['E_I'] = 100 - data["Extrovert-Introvert"]["score"]
final['S_N'] = 100 - data["Sensing-Intuition"]["score"]
final["T_F"] = 100 - data["Thinking-Feeling"]["score"]
final["J_P"] = 100 - data["Judging-Perceiving"]["score"]
except:
final = {}
return final
def generate_image(final):
if final == {}:
st.write("I have no idea about it. Please provide more information")
return
# 初始化画布和坐标轴
fig, ax = plt.subplots(figsize=(6, 6))
# 设置渐变颜色
colors = [
['#FF6347', '#FFD700'],
['#00FA9A', '#32CD32'],
['#6495ED', '#4682B4'],
['#DDA0DD', '#8A2BE2']
]
# 创建一个大的图像数组
img = np.zeros((100, 100, 3), dtype=np.uint8)
sorted_keys = ["E_I", "S_N", "T_F", "J_P"]
personality= ""
for k in sorted_keys:
v = final[k]
if v < 50:
personality += k[0]
else:
personality += k[2]
for idx, k in enumerate(sorted_keys):
v = final[k]
if idx == 0: # 左上
img[int(50*(1-v/100)):50, :50] = np.array([int(colors[idx][0][i:i+2], 16) for i in (1, 3, 5)])
img[:int(50*(1-v/100)), :50] = np.array([int(colors[idx][1][i:i+2], 16) for i in (1, 3, 5)])
elif idx == 1: # 右上
img[int(50*(1-v/100)):50, 50:] = np.array([int(colors[idx][0][i:i+2], 16) for i in (1, 3, 5)])
img[:int(50*(1-v/100)), 50:] = np.array([int(colors[idx][1][i:i+2], 16) for i in (1, 3, 5)])
elif idx == 2: # 左下
img[50+int(50*(1-v/100)):, :50] = np.array([int(colors[idx][0][i:i+2], 16) for i in (1, 3, 5)])
img[50:int(50*(1-v/100))+50, :50] = np.array([int(colors[idx][1][i:i+2], 16) for i in (1, 3, 5)])
else: # 右下
img[50+int(50*(1-v/100)):, 50:] = np.array([int(colors[idx][0][i:i+2], 16) for i in (1, 3, 5)])
img[50:int(50*(1-v/100))+50, 50:] = np.array([int(colors[idx][1][i:i+2], 16) for i in (1, 3, 5)])
ax.set_title(f'Your MBTI persopnality may be {personality}', fontsize=16)
ax.imshow(img)
ax.axis('off') # 不显示坐标轴
# 在每个部分中添加文本
for idx, k in enumerate(sorted_keys):
v = final[k]
if idx == 0:
ax.text(25, 25, f"{k[0]} {100-v:.0f}%\n{k[2]} {v:.0f}%", ha='center', va='center', color='black', fontsize=12)
elif idx == 1:
ax.text(75, 25, f"{k[0]} {100-v:.0f}%\n{k[2]} {v:.0f}%", ha='center', va='center', color='black', fontsize=12)
elif idx == 2:
ax.text(25, 75, f"{k[0]} {100-v:.0f}%\n{k[2]} {v:.0f}%", ha='center', va='center', color='black', fontsize=12)
else:
ax.text(75, 75, f"{k[0]} {100-v:.0f}%\n{k[2]} {v:.0f}%", ha='center', va='center', color='black', fontsize=12)
# 使用st.pyplot显示图表
st.pyplot(fig)
# def generate_image(final):
# # 初始化列表来保存标签和值
# labels = []
# values_l = []
# values_r = []
# sorted_keys = ["J_P","T_F","S_N", "E_I"]
# # 遍历字典，更新标签和值列表
# for k in sorted_keys:
# v = final[k]
# labels.append(k)
# values_l.append(100-v)
# values_r.append(v)
# personality = ''
# for k in sorted_keys:
# v = final[k]
# if v < 50:
# personality += k[0]
# else:
# personality += k[2]
# personality = personality[::-1]
# fig, ax = plt.subplots(figsize=(10,6))
# # 使用Seaborn的更漂亮的默认样式
# sns.set(style="whitegrid")
# # 为每个键绘制两个条形图，一个为E/S/T/J值，另一个为I/N/F/P值
# width = 0.4
# ind = range(len(labels))
# p1 = ax.barh(ind, values_l, width, color=sns.color_palette("coolwarm", 4)[2], label='E/S/T/J')
# p2 = ax.barh(ind, values_r, width, left=values_l, color=sns.color_palette("coolwarm", 4)[0], label='I/N/F/P')
# # 设置标签和标题
# ax.set_yticks(ind)
# ax.set_yticklabels([label[0] for label in labels])
# ax.set_xticks([0, 50, 100])
# ax.set_xlabel('Percentage', fontsize=14)
# ax.set_title(f'Your MBTI persopnality may be {personality}', fontsize=16)
# ax.legend(loc = 'upper right', bbox_to_anchor=(1.15, 1.15))
# # 显示右侧的'I/N/F/P'标签
# for i, label in enumerate(labels):
# ax.text(105, i, label[2], ha='center', va='center', color='black', fontsize=12)
# # 使用st.pyplot显示图表
# st.pyplot(fig)
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