hf space name

qwen_classifier/evaluate.py

@@ -1,5 +1,46 @@
 import numpy as np
 from sklearn.metrics import classification_report
+import zipfile
+import json
+import pandas as pd
+from .config import TAG_NAMES
+
+def load_data(test_data_path):
+    # zip file handler
+    zip_file = zipfile.ZipFile('code_classification_dataset.zip')
+
+    # list available files in the container
+    names = zip_file.namelist()
+    data = []
+    features = ["prob_desc_description","prob_desc_input_spec","prob_desc_output_spec"]
+    cols = features + ["tags"]
+    # extract a specific file from the zip container
+    for name in names[1:]:
+        f = zip_file.open(name)
+
+    # save the extraced file
+    content = f.read()
+    d = json.loads(content)
+    # json_fmt = json.dumps(d, indent=2)
+    # print(json_fmt)
+    row = []
+    for c in cols:
+        row.append(d[c])
+    data.append(row)
+    df = pd.DataFrame(data, columns=cols)
+    return df
+
+def preprocessing(df):
+    # Example dataset
+    texts = df["prob_desc_description"].values.tolist()
+    labels = df[TAG_NAMES].values.tolist()
+
+    # data:
+    # texts = ["text1", "text2", ...]  # list of texts
+    # labels = [[0,1,0,0,1,0,1,1,0], [0,1,1,0,0,0,0,0,0],, ...] # list of labels
+    
+    df = pd.DataFrame({'text':texts, 'labels': labels})
+
 
 def evaluate_model(test_data_path):
     # Load your test data

qwen_classifier/predict.py

@@ -32,7 +32,7 @@ def _predict_local(text, hf_repo):
 
 def _predict_hf_api(text, hf_token=None):
     # Use your Space endpoint instead of direct model API
-    SPACE_URL = "https://KeivanR/qwen-classifier-demo"
+    SPACE_URL = "https://keivanr-qwen-classifier-demo.hf.space"
     
     try:
         response = requests.post(

setup.py

@@ -10,7 +10,8 @@ setup(
         'click',
         'scikit-learn',
         'huggingface_hub',
-        'requests'
+        'requests',
+        'pandas'
     ],
     entry_points={
         'console_scripts': [