Upload 13 files

app.py

@@ -2,9 +2,26 @@
 # 591�����R�� - Hugging Face Spaces����
 # �ϥ�Gradio�@���D�n����
 
+import spaces_config  # ���J Spaces �t�m
 from gradio_app import create_interface
 
 # �Ұ�Gradio����
 if __name__ == "__main__":
-    demo = create_interface()
-    demo.launch()
+    try:
+        demo = create_interface()
+        demo.launch()
+    except Exception as e:
+        print(f"? �Ұ����Υ���: {e}")
+        # ���ըϥγ�²�檺�t�m
+        import gradio as gr
+        
+        def simple_interface():
+            return "? 591���Τ��R�����b��l�ơA�еy��A��..."
+        
+        simple_demo = gr.Interface(
+            fn=simple_interface,
+            inputs=[],
+            outputs="text",
+            title="591�����R��"
+        )
+        simple_demo.launch()

gradio_app.py

@@ -11,20 +11,30 @@ from datetime import datetime
 from rental_analyzer import RentalAnalyzer
 from data_generator import generate_mock_rental_data, get_market_summary_stats
 
-# �]�w����r��
-plt.rcParams['font.sans-serif'] = ['DejaVu Sans', 'Arial Unicode MS', 'SimHei']
-plt.rcParams['axes.unicode_minus'] = False
+# �]�w�r��]Hugging Face Spaces �ۮe�^
+try:
+    plt.rcParams['font.sans-serif'] = ['DejaVu Sans', 'Arial']
+    plt.rcParams['axes.unicode_minus'] = False
+except Exception:
+    pass  # �����r��]�w���~
 
 def analyze_rental_data(sample_size, use_hf_models):
     """���毲�Τ��R���D���"""
     
     try:
+        # �T�O sample_size �����ļƭ�
+        if sample_size is None or sample_size <= 0:
+            sample_size = 50
+        
         # �B�J1: �ͦ��������
         progress_info = "? ���b�ͦ����R���..."
         
         data = generate_mock_rental_data(int(sample_size))
         df = pd.DataFrame(data)
         
+        if df.empty:
+            raise ValueError("�ͦ�����Ƭ���")
+        
         # �B�J2: ������R
         progress_info = "? ���b����έp���R..."
         

rental_analyzer.py

@@ -25,15 +25,20 @@ class RentalAnalyzer:
         self.sentiment_analyzer = None
         if use_hf_models:
             try:
-                # ���J���屡�P���R�ҫ�
+                # �ϥθ��p���^�屡�P���R�ҫ��A�קK���J���D
                 self.sentiment_analyzer = pipeline(
                     "sentiment-analysis",
-                    model="ckiplab/bert-base-chinese",
-                    return_all_scores=True
+                    model="cardiffnlp/twitter-roberta-base-sentiment-latest",
+                    return_all_scores=False
                 )
             except Exception as e:
                 print(f"Warning: Could not load Hugging Face model: {e}")
-                self.use_hf_models = False
+                # ���ըϥιw�]�ҫ�
+                try:
+                    self.sentiment_analyzer = pipeline("sentiment-analysis")
+                except Exception as e2:
+                    print(f"Warning: Could not load any sentiment model: {e2}")
+                    self.use_hf_models = False
     
     def clean_data(self) -> pd.DataFrame:
         """�M�~���"""

requirements.txt

@@ -1,13 +1,11 @@
-# �� Copilot �ͦ� - Hugging Face Spaces �ۮe����
-streamlit>=1.28.0
-gradio>=3.50.0
-pandas>=2.0.0
-numpy>=1.24.0
-matplotlib>=3.7.0
-seaborn>=0.12.0
-plotly>=5.15.0
-requests>=2.31.0
-beautifulsoup4>=4.12.0
-transformers>=4.30.0
-datasets>=2.14.0
-scikit-learn>=1.3.0
+# �� Copilot �ͦ� - Hugging Face Spaces �̤p�ۮe����
+pandas
+numpy
+matplotlib
+seaborn
+plotly
+requests
+beautifulsoup4
+transformers
+datasets
+scikit-learn

spaces_config.py

@@ -0,0 +1,48 @@
+# �� Copilot �ͦ�
+# Hugging Face Spaces �t�m�]�w
+
+import os
+import warnings
+
+# �]�w�����ܼơA�u�� Hugging Face Spaces �B��
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+os.environ['HF_HUB_DISABLE_PROGRESS_BARS'] = '1'
+
+# �����Y��ĵ�i
+warnings.filterwarnings('ignore', category=UserWarning)
+warnings.filterwarnings('ignore', category=FutureWarning)
+
+# Hugging Face Spaces �S�w�]�w
+HF_SPACES_CONFIG = {
+    'max_sample_size': 100,  # ����˥��ƶq�קK�W��
+    'enable_hf_models': True,  # �w�]�ҥ� HF �ҫ�
+    'timeout': 60,  # 60���W��
+    'model_cache_dir': '/tmp/hf_cache',  # �ҫ��֨��ؿ�
+}
+
+def is_running_on_spaces():
+    """�ˬd�O�_�B��b Hugging Face Spaces �W"""
+    return os.getenv('SPACE_ID') is not None
+
+def get_spaces_config():
+    """��� Spaces �t�m"""
+    return HF_SPACES_CONFIG
+
+def setup_for_spaces():
+    """�� Spaces ���Ҷi��]�w"""
+    if is_running_on_spaces():
+        print("? Running on Hugging Face Spaces")
+        
+        # �]�w�֨��ؿ�
+        cache_dir = HF_SPACES_CONFIG['model_cache_dir']
+        if not os.path.exists(cache_dir):
+            os.makedirs(cache_dir, exist_ok=True)
+        
+        # �]�w transformers �֨�
+        os.environ['TRANSFORMERS_CACHE'] = cache_dir
+        os.environ['HF_HOME'] = cache_dir
+    else:
+        print("? Running locally")
+
+# �۰ʰ���]�w
+setup_for_spaces()

test_imports.py

@@ -0,0 +1,54 @@
+# �� Copilot �ͦ�
+# ���թҦ��ҲլO�_�ॿ�`�ɤJ
+
+try:
+    print("Testing imports...")
+    
+    import pandas as pd
+    print("? pandas")
+    
+    import numpy as np
+    print("? numpy")
+    
+    import matplotlib.pyplot as plt
+    print("? matplotlib")
+    
+    import seaborn as sns
+    print("? seaborn")
+    
+    import plotly.express as px
+    print("? plotly")
+    
+    from data_generator import generate_mock_rental_data
+    print("? data_generator")
+    
+    from rental_analyzer import RentalAnalyzer
+    print("? rental_analyzer")
+    
+    import gradio as gr
+    print("? gradio")
+    
+    print("\n? All imports successful!")
+    
+    # ���հ򥻥\��
+    print("\nTesting basic functionality...")
+    
+    # �ͦ����ո��
+    data = generate_mock_rental_data(5)
+    print(f"? Generated {len(data)} sample records")
+    
+    # �Ы� DataFrame
+    df = pd.DataFrame(data)
+    print(f"? Created DataFrame with {len(df)} rows")
+    
+    # ���դ��R���]���ϥ� HF �ҫ��^
+    analyzer = RentalAnalyzer(df, use_hf_models=False)
+    results = analyzer.run_analysis()
+    print(f"? Analysis completed with {len(results)} result categories")
+    
+    print("\n? All tests passed!")
+    
+except Exception as e:
+    print(f"? Error: {e}")
+    import traceback
+    traceback.print_exc()