试着使用gradio

README.md

@@ -5,7 +5,7 @@ colorFrom: indigo
 colorTo: purple
 sdk: streamlit
 sdk_version: 1.31.1
-app_file: app.py
+app_file: app_history.py
 pinned: false
 license: mit
 ---

app.py

@@ -1,54 +1,3 @@
-from pathlib import Path
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-import os
-from transformers import AutoTokenizer, AutoModel
-import requests
+import gradio as gr
 
-# Assuming you have set the HF_TOKEN environment variable with your Hugging Face token
-huggingface_token = os.getenv('HF_TOKEN')
-# Set up the token to use with the Hugging Face API
-if huggingface_token is not None:
-    os.environ['HUGGINGFACE_CO_API_TOKEN'] = huggingface_token
-    API_URL = "https://api-inference.huggingface.co/models/Tokymin/Mood_Anxiety_Disorder_Classify_Model"
-    headers = {"Authorization": f"Tokymin {huggingface_token}"}
-else:
-    print("error, no token")
-    exit(0)
-
-# def query(payload):
-#     response = requests.post(API_URL, headers=headers, json=payload)
-#     return response.json()
-# data = query("Can you please let us know more details about your ")
-path: Path = Path('Tokymin/Mood_Anxiety_Disorder_Classify_Model')
-tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=path, cache_dir='/home/user', token=huggingface_token)
-
-# tokenizer = AutoTokenizer.from_pretrained('Tokymin/Mood_Anxiety_Disorder_Classify_Model')
-model = AutoModelForSequenceClassification.from_pretrained("Tokymin/Mood_Anxiety_Disorder_Classify_Model",num_labels=8)
-model.eval()
-
-
-def predict(text):
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-    probabilities = torch.softmax(logits, dim=1).squeeze()
-    # 假设每个类别（SAS_Class和SDS_Class）都有4个概率值
-    sas_probs = probabilities[:4]  # 获取SAS_Class的概率
-    sds_probs = probabilities[4:]  # 获取SDS_Class的概率
-    return sas_probs, sds_probs
-
-
-# 创建Streamlit应用
-st.title("Multi-label Classification App")
-
-# 用户输入文本
-user_input = st.text_area("Enter text here", "Type something...")
-
-if st.button("Predict"):
-    # 显示预测结果
-    sas_probs, sds_probs = predict(user_input)
-    st.write("SAS_Class probabilities:", sas_probs.numpy())
-    st.write("SDS_Class probabilities:", sds_probs.numpy())
+gr.load("models/Tokymin/Mood_Anxiety_Disorder_Classify_Model").launch()

app_history.py

@@ -0,0 +1,54 @@
+from pathlib import Path
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import os
+from transformers import AutoTokenizer, AutoModel
+import requests
+
+# Assuming you have set the HF_TOKEN environment variable with your Hugging Face token
+huggingface_token = os.getenv('HF_TOKEN')
+# Set up the token to use with the Hugging Face API
+if huggingface_token is not None:
+    os.environ['HUGGINGFACE_CO_API_TOKEN'] = huggingface_token
+    API_URL = "https://api-inference.huggingface.co/models/Tokymin/Mood_Anxiety_Disorder_Classify_Model"
+    headers = {"Authorization": f"Tokymin {huggingface_token}"}
+else:
+    print("error, no token")
+    exit(0)
+
+# def query(payload):
+#     response = requests.post(API_URL, headers=headers, json=payload)
+#     return response.json()
+# data = query("Can you please let us know more details about your ")
+path: Path = Path('Tokymin/Mood_Anxiety_Disorder_Classify_Model')
+tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=path, cache_dir='/home/user', token=huggingface_token)
+
+# tokenizer = AutoTokenizer.from_pretrained('Tokymin/Mood_Anxiety_Disorder_Classify_Model')
+model = AutoModelForSequenceClassification.from_pretrained("Tokymin/Mood_Anxiety_Disorder_Classify_Model",num_labels=8)
+model.eval()
+
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+    probabilities = torch.softmax(logits, dim=1).squeeze()
+    # 假设每个类别（SAS_Class和SDS_Class）都有4个概率值
+    sas_probs = probabilities[:4]  # 获取SAS_Class的概率
+    sds_probs = probabilities[4:]  # 获取SDS_Class的概率
+    return sas_probs, sds_probs
+
+
+# 创建Streamlit应用
+st.title("Multi-label Classification App")
+
+# 用户输入文本
+user_input = st.text_area("Enter text here", "Type something...")
+
+if st.button("Predict"):
+    # 显示预测结果
+    sas_probs, sds_probs = predict(user_input)
+    st.write("SAS_Class probabilities:", sas_probs.numpy())
+    st.write("SDS_Class probabilities:", sds_probs.numpy())