last_version_upload to huggingface

app.py

@@ -11,20 +11,20 @@ except RuntimeError:
 sys.path.append(str(Path(__file__).parent))
 
 
-from pages import emotion_analyzer # 导入情绪分析页面和 Chatbot 页面
-
 def main():
     st.set_page_config(
         page_title="Audio Emotion Recognition System",
         page_icon="🎵",
         layout="wide"
     )
-    
-    st.title("Audio Emotion Recognition System")
-    st.write("This is a web application for audio emotion recognition.")
-    
-    # 只测试情绪分析页面
-    emotion_analyzer.show()
+    st.sidebar.title("Navigation Bar")
+    st.sidebar.markdown("<small>(Chatbot is not available now, update soon...😉)</small>", unsafe_allow_html=True)
+    app_mode = st.sidebar.radio("Go to", ["Emotion Analyzer", "Chatbot"])
+    if app_mode == "Emotion Analyzer":
+        from emotion_analyzer import show  # 直接导入模块
+        show()
+    elif app_mode == "Chatbot":
+        st.write("Chatbot is not available now, update soon...😉")
 
 if __name__ == "__main__":
     main() 

pages/chatbot.py → chatbot.py

@@ -1,97 +1,97 @@
-import streamlit as st
-import torch
-import torchaudio
-import json
-from openai import AzureOpenAI
-from openai.types.beta.threads import Message
-from safetensors.torch import load_file
-from transformers import AutoTokenizer, Wav2Vec2Processor, BertModel, Wav2Vec2Model
-from huggingface_hub import hf_hub_download
-from dotenv import load_dotenv
-from utils import model_inference
-import os
-
-# 加载环境变量
-load_dotenv("Group7/.env")
-api_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
-api_key = os.getenv("AZURE_OPENAI_API_KEY")
-api_version = os.getenv("AZURE_OPENAI_API_VERSION")
-api_deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT")
-
-# 初始化 OpenAI 客户端
-client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_endpoint)
-
-# 设定 Chatbot 角色
-instruction = (
-    "You are a psychiatrist talking to a patient who may be depressed. "
-    "You'll receive their emotional state and conversation text. "
-    "Your goal is to help them open up and guide them to a positive path. "
-    "Be friendly, professional, empathetic, and supportive."
-)
-
-# 设定 Chatbot 线程和助手
-if "thread" not in st.session_state:
-    st.session_state.thread = client.beta.threads.create()
-
-if "assistant" not in st.session_state:
-    assistant_id = "asst_Sb1W9jVTeL1iyzu6N5MilgA1"
-    try:
-        st.session_state.assistant = client.beta.assistants.retrieve(assistant_id=assistant_id)
-    except:
-        st.session_state.assistant = client.beta.assistants.create(
-            name="Depression Chatbot",
-            instructions=instruction,
-            model=api_deployment_name,
-        )
-
-# 发送消息到 Azure Chatbot
-def send_message_to_chatbot(user_input, emotion):
-    chat_history = client.beta.threads.messages.list(thread_id=st.session_state.thread.id)
-    messages = [{"role": msg.role, "content": msg.content} for msg in chat_history]
-    
-    messages.append({"role": "user", "content": f"Emotion: {emotion}. {user_input}"})
-
-    client.beta.threads.messages.create(
-        thread_id=st.session_state.thread.id,
-        role="user",
-        content=f"Emotion: {emotion}. {user_input}",
-    )
-
-    run = client.beta.threads.runs.create(
-        thread_id=st.session_state.thread.id,
-        assistant_id=st.session_state.assistant.id,
-    )
-
-    while run.status in ["queued", "in_progress"]:
-        run = client.beta.threads.runs.retrieve(run.id)
-
-    response_messages = client.beta.threads.messages.list(thread_id=st.session_state.thread.id)
-    return response_messages[-1].content if response_messages else "No response."
-
-# Streamlit 界面
-st.title("🧠 AI Depression Chatbot")
-
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-
-# 用户输入
-user_input = st.text_input("Enter your message:")
-audio_file = st.file_uploader("Upload audio file", type=["wav", "mp3"])
-
-if st.button("Send"):
-    if user_input or audio_file:
-        emotion_probabilities = model_inference.predict_emotion(user_input, audio_file)
-        dominant_emotion = max(emotion_probabilities, key=emotion_probabilities.get)
-
-        chatbot_response = send_message_to_chatbot(user_input, dominant_emotion)
-        
-        # 保存聊天记录
-        st.session_state.chat_history.append({"role": "user", "content": user_input})
-        st.session_state.chat_history.append({"role": "assistant", "content": chatbot_response})
-
-        # 显示聊天记录
-        for chat in st.session_state.chat_history:
-            st.write(f"**{chat['role'].capitalize()}**: {chat['content']}")
-
-    else:
-        st.warning("Please enter a message or upload an audio file.")
+import streamlit as st
+import torch
+import torchaudio
+import json
+from openai import AzureOpenAI
+from openai.types.beta.threads import Message
+from safetensors.torch import load_file
+from transformers import AutoTokenizer, Wav2Vec2Processor, BertModel, Wav2Vec2Model
+from huggingface_hub import hf_hub_download
+from dotenv import load_dotenv
+from utils import model_inference
+import os
+
+# 加载环境变量
+load_dotenv(r"Group7/.env")
+api_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+api_key = os.getenv("AZURE_OPENAI_API_KEY")
+api_version = os.getenv("AZURE_OPENAI_API_VERSION")
+api_deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT")
+
+# 初始化 OpenAI 客户端
+client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_endpoint)
+
+# 设定 Chatbot 角色
+instruction = (
+    "You are a psychiatrist talking to a patient who may be depressed. "
+    "You'll receive their emotional state and conversation text. "
+    "Your goal is to help them open up and guide them to a positive path. "
+    "Be friendly, professional, empathetic, and supportive."
+)
+
+# 设定 Chatbot 线程和助手
+if "thread" not in st.session_state:
+    st.session_state.thread = client.beta.threads.create()
+
+if "assistant" not in st.session_state:
+    assistant_id = "asst_Sb1W9jVTeL1iyzu6N5MilgA1"
+    try:
+        st.session_state.assistant = client.beta.assistants.retrieve(assistant_id=assistant_id)
+    except:
+        st.session_state.assistant = client.beta.assistants.create(
+            name="Depression Chatbot",
+            instructions=instruction,
+            model=api_deployment_name,
+        )
+
+# 发送消息到 Azure Chatbot
+def send_message_to_chatbot(user_input, emotion):
+    chat_history = client.beta.threads.messages.list(thread_id=st.session_state.thread.id)
+    messages = [{"role": msg.role, "content": msg.content} for msg in chat_history]
+    
+    messages.append({"role": "user", "content": f"Emotion: {emotion}. {user_input}"})
+
+    client.beta.threads.messages.create(
+        thread_id=st.session_state.thread.id,
+        role="user",
+        content=f"Emotion: {emotion}. {user_input}",
+    )
+
+    run = client.beta.threads.runs.create(
+        thread_id=st.session_state.thread.id,
+        assistant_id=st.session_state.assistant.id,
+    )
+
+    while run.status in ["queued", "in_progress"]:
+        run = client.beta.threads.runs.retrieve(run.id)
+
+    response_messages = client.beta.threads.messages.list(thread_id=st.session_state.thread.id)
+    return response_messages[-1].content if response_messages else "No response."
+
+# Streamlit 界面
+st.title("🧠 AI Depression Chatbot")
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# 用户输入
+user_input = st.text_input("Enter your message:")
+audio_file = st.file_uploader("Upload audio file", type=["wav", "mp3"])
+
+if st.button("Send"):
+    if user_input or audio_file:
+        emotion_probabilities = model_inference.predict_emotion(user_input, audio_file)
+        dominant_emotion = max(emotion_probabilities, key=emotion_probabilities.get)
+
+        chatbot_response = send_message_to_chatbot(user_input, dominant_emotion)
+        
+        # 保存聊天记录
+        st.session_state.chat_history.append({"role": "user", "content": user_input})
+        st.session_state.chat_history.append({"role": "assistant", "content": chatbot_response})
+
+        # 显示聊天记录
+        for chat in st.session_state.chat_history:
+            st.write(f"**{chat['role'].capitalize()}**: {chat['content']}")
+
+    else:
+        st.warning("Please enter a message or upload an audio file.")

components/visualizations.py

@@ -27,11 +27,9 @@ def plot_emotion_distribution(emotion_dict):
         polar=dict(
             radialaxis=dict(
                 visible=True,
-                range=[0, 0.3],  # 设置范围
-                tickvals=[0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3],  # 设置刻度
-                ticktext=['0%', '5%', '10%', '15%', '20%', '25%', '30%']  # 设置刻度标签
+                range=[0, 1]  # 设置范围
             )),
         showlegend=False
     )
 
-    st.plotly_chart(fig, use_container_width=True)
+    st.plotly_chart(fig, use_container_width=True) 

emotion_analyzer.py

@@ -0,0 +1,162 @@
+import streamlit as st
+from components.visualizations import plot_emotion_distribution
+from utils import model_inference
+from components.audio_player import play_audio
+from components.debug_tools import DebugTools
+import json
+import os
+import time
+
+# ✅ 初始化 session_state 以存储分析结果和反馈状态
+if "emotion_result" not in st.session_state:
+    st.session_state.emotion_result = None
+if "feedback_submitted" not in st.session_state:
+    st.session_state.feedback_submitted = False
+if "user_feedback" not in st.session_state:
+    st.session_state.user_feedback = ""
+
+def show_history():
+    """📜 显示历史记录（最近 5 条）"""
+    history_file = "history\history.json"
+    
+    if os.path.exists(history_file):
+        with open(history_file, 'r') as f:
+            history = json.load(f)
+        
+        if history:
+            st.subheader("📜 History")
+            with st.expander("Click to view past records", expanded=False):
+                for record in history[-5:]:  # 只显示最近 5 条
+                    st.write(f"🎧 **Audio File**: {record['audio_file']}")
+                    st.write(f"📝 **Transcript**: {record['transcript']}")
+                    st.write(f"🎭 **Emotions**: {record['emotions']}")
+                    st.write("---")
+        else:
+            st.write("🚫 No history records found.")
+    else:
+        st.write("🚫 No history file.")
+
+def show():
+    st.markdown(
+        "<h1 style='text-align: center; color: #FF5733;'>😉 Emotion Analyzer 🎭</h1>",
+        unsafe_allow_html=True
+    )
+
+    # 📜 显示历史记录
+    show_history()
+    
+    # 🛠️ 初始化调试工具
+    debug = DebugTools()
+    debug.show_debug_info()
+    
+    # 🎤 让用户上传音频和文本输入
+    st.markdown("---")
+    col1, col2 = st.columns(2)
+    with col1:
+        audio_file = st.file_uploader("Upload your audio file", type=['wav', 'mp3'])
+    # 🎧 如果音频存在，则显示音频信息
+        if audio_file is not None:
+            debug.show_audio_info(audio_file)
+            st.audio(audio_file, format="audio/mp3")
+    with col2:
+        text_input = st.text_input("Enter your text input")
+        transcript = text_input  # 默认情况下，文本输入即为转写结果
+        st.write("📄 **Audio transcript:**", transcript)
+
+    # 🎯 **如果用户删除了音频，则自动重置情绪分析结果**
+    if audio_file is None and "emotion_result" in st.session_state and st.session_state.emotion_result is not None:
+        st.warning("🚫 Audio file removed. Resetting analysis results.")
+        st.session_state.emotion_result = None
+
+    # # 🎧 如果音频存在，则显示音频信息
+    # if audio_file is not None:
+    #     debug.show_audio_info(audio_file)
+    #     st.audio(audio_file, format="audio/mp3")
+    
+    if audio_file is not None and text_input:
+        if st.button("Analyse Your Emotion! 😊"):
+            st.session_state.feedback_submitted = False  # 重置反馈状态
+
+            # 📊 显示进度条
+            progress_bar = st.progress(0)
+            for percent in range(0, 101, 20):
+                progress_bar.progress(percent)
+                time.sleep(0.2)  # 提高加载体验
+
+            try:
+                transcript = text_input
+
+                # 🔍 执行情绪分析
+                with st.spinner("Analysing emotion..."):
+                    for percent in range(30, 101, 20):  
+                        progress_bar.progress(percent)
+                        time.sleep(0.2)  # 避免过快闪过
+                    emotions = model_inference.predict_emotion(text_input, audio_file)
+
+                if emotions:
+                    best_emotion = max(emotions, key=lambda x: float(emotions[x].strip('%')))
+                    # 🎭 显示情绪分析结果
+                    st.markdown("---")
+                    st.markdown("### 🎭 Emotion Analysis Results")
+                    st.success(f"🎭 **Predicted Emotion:** {best_emotion}")
+                    st.success(f"📊 **Emotion Probabilities:** {emotions}")
+
+                    # ✅ 存储分析结果，防止页面刷新导致结果丢失
+                    st.session_state.emotion_result = {
+                        "best_emotion": best_emotion,
+                        "emotions": emotions,
+                        "transcript": transcript
+                    }
+
+            except Exception as e:
+                debug.log_error(e, context=f"Processing file: {audio_file.name}")
+
+    # 🎭 只有当有分析结果时，才显示结果
+    if st.session_state.emotion_result:
+        best_emotion = st.session_state.emotion_result["best_emotion"]
+        emotions = st.session_state.emotion_result["emotions"]
+
+        # 🎈 Streamlit 内置动画（仅用于 "Joy" 和 "Sad"）
+        if best_emotion == "Joy":
+            st.balloons()
+            st.markdown("**Yay! You seem so happy! 🎉😊**")
+            st.write("Keep smiling, it's contagious! 🌟")
+        elif best_emotion == "Sad":
+            st.snow()
+            st.markdown("**Oh no, you seem a bit down... 😔💭**")
+            st.write("I'm here if you need to talk or cheer up! 💬💖")
+        else:
+            emotion_messages = {
+                "Angry": ["😡 **Whoa! Looks like you're really angry...**", "Take a deep breath, let's cool off together! 🧘‍♂️"],
+                "Surprised": ["🤯 **Wow, you're really surprised!**", "That's a big reaction! Hope it's a good surprise! 🎉"],
+                "Fearful": ["😨 **Looks like you're feeling scared...**", "It's okay to be scared. I'm here to help! 💪"],
+                "Disgusted": ["🤢 **Yikes, you're feeling disgusted...**", "That doesn't sound pleasant. Let's turn it around! 🌈"],
+                "Neutral": ["🤔 **Hmm, your emotions seem mixed...**", "Can you tell me more? Let's figure this out together! 💬"]
+            }
+            if best_emotion in emotion_messages:
+                st.markdown(emotion_messages[best_emotion][0])
+                st.write(emotion_messages[best_emotion][1])
+
+        # 📊 显示情绪分布图
+        plot_emotion_distribution(emotions)
+    
+        # 💾 保存历史记录
+        if audio_file is not None:
+            model_inference.save_history(audio_file, transcript, emotions)
+        else:
+            st.warning("⚠️ No audio file uploaded. History not saved.")
+
+    # 📣 提供反馈功能
+    if st.session_state.emotion_result:
+        st.subheader("📣 Give us your feedback!")
+        feedback = st.radio("Do you agree with the prediction?", ["😀 Great!", "😐 Okay", "😡 Wrong"], index=1)
+        user_feedback = st.text_area("Tell us more (optional):", st.session_state.user_feedback)
+
+        if st.button("Submit Feedback"):
+            st.session_state.user_feedback = user_feedback
+            st.session_state.feedback_submitted = True
+
+        if st.session_state.feedback_submitted:
+            st.success("✅ Thank you for your feedback! It has been recorded.")
+
+

history/history.json

@@ -1,142 +0,0 @@
-[
-    {
-        "audio_file": "test.wav",
-        "transcript": "please go now and make sure they stay from away, come back as soon as you can",
-        "emotions": {
-            "Neutral": "11.89%",
-            "Joy": "16.05%",
-            "Sad": "14.53%",
-            "Angry": "14.88%",
-            "Surprised": "14.02%",
-            "Fearful": "14.50%",
-            "Disgusted": "14.13%"
-        },
-        "probabilities": null
-    },
-    {
-        "audio_file": "test.wav",
-        "transcript": "please go now and make sure they stay from away, come back as soon as you can",
-        "emotions": {
-            "Neutral": "11.89%",
-            "Joy": "16.05%",
-            "Sad": "14.53%",
-            "Angry": "14.88%",
-            "Surprised": "14.02%",
-            "Fearful": "14.50%",
-            "Disgusted": "14.13%"
-        },
-        "probabilities": null
-    },
-    {
-        "audio_file": "test.wav",
-        "transcript": "please go now and make sure they stay from away, come back as soon as you can",
-        "emotions": {
-            "Neutral": "11.89%",
-            "Joy": "16.05%",
-            "Sad": "14.53%",
-            "Angry": "14.88%",
-            "Surprised": "14.02%",
-            "Fearful": "14.50%",
-            "Disgusted": "14.13%"
-        },
-        "probabilities": null
-    },
-    {
-        "audio_file": "test.wav",
-        "transcript": "please go now and make sure they stay from away, come back as soon as you can",
-        "emotions": {
-            "Neutral": "11.89%",
-            "Joy": "16.05%",
-            "Sad": "14.53%",
-            "Angry": "14.88%",
-            "Surprised": "14.02%",
-            "Fearful": "14.50%",
-            "Disgusted": "14.13%"
-        },
-        "probabilities": null
-    },
-    {
-        "audio_file": "test.wav",
-        "transcript": "please go now and make sure they stay from away, come back as soon as you can",
-        "emotions": {
-            "Neutral": "11.89%",
-            "Joy": "16.05%",
-            "Sad": "14.53%",
-            "Angry": "14.88%",
-            "Surprised": "14.02%",
-            "Fearful": "14.50%",
-            "Disgusted": "14.13%"
-        },
-        "probabilities": null
-    },
-    {
-        "audio_file": "test.wav",
-        "transcript": "Please go now, and make sure to stay safe on the way. Come back as soon as you can.",
-        "emotions": {
-            "Neutral": "11.93%",
-            "Joy": "16.90%",
-            "Sad": "14.75%",
-            "Angry": "14.36%",
-            "Surprised": "14.24%",
-            "Fearful": "14.24%",
-            "Disgusted": "13.57%"
-        },
-        "probabilities": null
-    },
-    {
-        "audio_file": "test.wav",
-        "transcript": "Please go now, and make sure to stay safe on the way. Come back as soon as you can.",
-        "emotions": {
-            "Neutral": "54.73%",
-            "Joy": "5.80%",
-            "Sad": "29.00%",
-            "Angry": "2.24%",
-            "Surprised": "0.51%",
-            "Fearful": "6.93%",
-            "Disgusted": "0.78%"
-        },
-        "probabilities": null
-    },
-    {
-        "audio_file": "en-US-DavisNeural_sad_2x(1).wav",
-        "transcript": "I am fine. Just thinking about some things, that's all.",
-        "emotions": {
-            "Neutral": "12.95%",
-            "Joy": "42.23%",
-            "Sad": "41.40%",
-            "Angry": "0.25%",
-            "Surprised": "0.75%",
-            "Fearful": "2.24%",
-            "Disgusted": "0.19%"
-        },
-        "probabilities": null
-    },
-    {
-        "audio_file": "en-US-NancyNeural_sad_2x(1).wav",
-        "transcript": "I am fine. Just thinking about some things, that's all.",
-        "emotions": {
-            "Neutral": "13.23%",
-            "Joy": "43.65%",
-            "Sad": "39.48%",
-            "Angry": "0.28%",
-            "Surprised": "0.79%",
-            "Fearful": "2.35%",
-            "Disgusted": "0.22%"
-        },
-        "probabilities": null
-    },
-    {
-        "audio_file": "301_26.wav",
-        "transcript": "stayed in school you know not let the accident just distract me and not i wouldve remained in school i probably wouldnt have had kids early you know um",
-        "emotions": {
-            "Neutral": "16.10%",
-            "Joy": "0.46%",
-            "Sad": "81.54%",
-            "Angry": "0.30%",
-            "Surprised": "0.18%",
-            "Fearful": "1.24%",
-            "Disgusted": "0.17%"
-        },
-        "probabilities": null
-    }
-]

pages/__init__.py

@@ -1 +0,0 @@
-# 空文件，使pages成为一个Python包 

pages/emotion_analyzer.py

@@ -1,80 +0,0 @@
-import streamlit as st
-from components.visualizations import plot_emotion_distribution
-from utils import model_inference
-from components.audio_player import play_audio
-from components.debug_tools import DebugTools
-import json
-import os
-
-def show_history():
-    """显示历史记录"""
-    history_file = "history.json"
-    
-    if os.path.exists(history_file):
-        with open(history_file, 'r') as f:
-            history = json.load(f)
-        
-        if history:
-            st.subheader("History")
-            for record in history:
-                st.write(f"Audio File: {record['audio_file']}")
-                st.write(f"Transcript: {record['transcript']}")
-                st.write(f"Emotions: {record['emotions']}")
-                st.write(f"Probabilities: {record['probabilities']}")
-                st.write("---")
-        else:
-            st.write("No history records.")
-    else:
-        st.write("No history file.")
-
-def show():
-    st.header("Emotion Analyzer")
-    
-    # 显示历史记录
-    show_history()
-    
-    # 初始化调试工具
-    debug = DebugTools()
-    
-    # 显示系统调试信息
-    debug.show_debug_info()
-    
-    # 文件上传
-    audio_file = st.file_uploader("Upload audio file", type=['wav', 'mp3'])
-    text_input = st.text_input("Enter text input")
-
-    if audio_file is not None and text_input:
-        # 显示音频文件信息
-        debug.show_audio_info(audio_file)
-        
-        # 使用audio_player组件
-        play_audio(audio_file)
-        
-        if st.button("Analyse Your Emotion!😊"):
-            # 显示进度条
-            progress_bar = st.progress(0)
-            
-            try:
-                # 直接使用用户输入的文本作为转写
-                transcript = text_input
-                st.write("Audio transcript:", transcript)
-                
-                # 2. 情绪分析
-                with st.spinner("Analysing emotion..."):
-                    progress_bar.progress(30)
-                    emotions = model_inference.predict_emotion(text_input, audio_file)
-                
-                # 3. 显示结果
-                progress_bar.progress(30)
-                
-                # 显示预测结果
-                st.success(f"Predict: {emotions}")
-                
-                # 显示情绪概率分布图
-                plot_emotion_distribution(emotions)
-                
-                # 保存历史记录
-                model_inference.save_history(audio_file, transcript, emotions, None)  # 这里可以根据需要调整
-                
-            except Exception as e:
-                debug.log_error(e, context=f"Processing file: {audio_file.name}")

utils/model_inference.py

@@ -71,6 +71,7 @@ class MultimodalClassifier(nn.Module):
         audio_features = audio_outputs.hidden_states[-1][:, 0, :]
 
         # **拼接特征**
+
         combined_features = torch.cat((text_features, audio_features), dim=-1)
 
         # **分类**
@@ -98,13 +99,11 @@ tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 
 def preprocess_text(text):
     text_inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
-    print(text_inputs)
     return text_inputs.to(device)
 
 def preprocess_audio(audio_path):
     waveform, sample_rate = torchaudio.load(audio_path)
     waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
-    print(waveform)
     return waveform.to(device)
 
 labels = ["Neutral", "Joy", "Sad", "Angry", "Surprised", "Fearful", "Disgusted"]
@@ -123,7 +122,7 @@ def generate_transcript(audio_file):
     """生成音频的文字转写"""
     return audio_file.name  # 直接返回音频文件的名称
 
-def save_history(audio_file, transcript, emotions, probabilities):
+def save_history(audio_file, transcript, emotions):
     """保存分析历史记录到文件"""
     history_file = r"history/history.json"
     
@@ -138,7 +137,6 @@ def save_history(audio_file, transcript, emotions, probabilities):
         "audio_file": audio_file.name,
         "transcript": transcript,
         "emotions": emotions,
-        "probabilities": probabilities
     })
     
     with open(history_file, 'w') as f: