Create process.py

process.py

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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)
+    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"]
+    return final
+    
+
+
+def generate_image(final):
+    # 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"]
+
+    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.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)
+