Update app.py

app.py

@@ -1,4 +1,3 @@
-# Install necessary libraries
 import os
 import subprocess
 
@@ -21,7 +20,6 @@ from sklearn.model_selection import train_test_split
 
 # Function to convert a list to a DataFrame
 def list_to_dataframe(data_list):
-    # Convert the list to a DataFrame (assuming it's a list of dicts or tuples)
     df = pd.DataFrame(data_list)
     return df
 
@@ -37,7 +35,6 @@ def load_dataset(file_path=None):
         default_data = [
             {'text': 'Example sentence 1', 'label': 'label1'},
             {'text': 'Example sentence 2', 'label': 'label2'},
-            # Add more example data as needed
         ]
         return list_to_dataframe(default_data)
     
@@ -52,55 +49,48 @@ def load_dataset(file_path=None):
 # Preprocess the data
 def preprocess_data(df):
     # Add your preprocessing steps here
-    # For example: cleaning, tokenization, etc.
     return df
 
 # Train your model
 def train_model(df):
-    # Split the dataset into training and testing sets
     train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
     
     # Load your pre-trained model and tokenizer from Hugging Face
     tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
     model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
 
-    # Add your training code here
-    # This may involve tokenizing the data and feeding it into the model
+    # Training code placeholder
     return model
 
 # Define the Gradio interface function
 def predict(input_text):
-    # Load the model and tokenizer
     tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
     model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
     
-    # Tokenize input and make predictions
     inputs = tokenizer(input_text, return_tensors="pt")
     with torch.no_grad():
         outputs = model(**inputs)
     
-    # Process the outputs as needed (e.g., extracting relevant information)
     return outputs.last_hidden_state
 
 # Build the Gradio interface
 def build_interface(file_path=None):
-    df = load_dataset(file_path)  # Load your dataset
+    df = load_dataset(file_path)
     if df is None:
         return None
 
-    df = preprocess_data(df)  # Preprocess the dataset
-    model = train_model(df)  # Train your model
+    df = preprocess_data(df)
+    model = train_model(df)
     
     iface = gr.Interface(
         fn=predict,
-        inputs=gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
+        inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
         outputs="text"
     )
     return iface
 
 # Run the Gradio interface
 if __name__ == "__main__":
-    # You can specify a file_path here if you have a specific file to use
     file_path = None  # Change this to your specific file path if needed
     iface = build_interface(file_path=file_path)
     if iface: