Update app.py

app.py

@@ -3,75 +3,75 @@ import pandas as pd
 import os
 import subprocess
 import sys
-   
-   # Install spaCy model
-   os.system("python -m spacy download en_core_web_sm")
-   
-   def process_tweets(files, reset_processing=False):
-       # Save uploaded files
-       file_paths = []
-       for file in files:
-           if file.name.endswith('.csv'):
-               # Ensure directory exists
-               os.makedirs("projects_twitter_post", exist_ok=True)
-               
-               # Save file to the directory
-               dest_path = f"projects_twitter_post/{os.path.basename(file.name)}"
-               os.system(f"cp {file.name} {dest_path}")
-               file_paths.append(dest_path)
-       
-       if not file_paths:
-           return "No CSV files uploaded. Please upload CSV files containing tweet data."
-       
-       # Run the processing script
-       reset_flag = "--reset" if reset_processing else ""
-       result = subprocess.run(
-           f"python process_tweet_huggingface.py {reset_flag}",
-           shell=True,
-           capture_output=True,
-           text=True
-       )
-       
-       # Check if output files were created
-       output_files = []
-       for file_path in file_paths:
-           base_name = os.path.basename(file_path).replace('.csv', '')
-           processed_path = f"projects_twitter_post/{base_name}_processed.csv"
-           analysis_path = f"projects_twitter_post/{base_name}_analysis.csv"
-           
-           if os.path.exists(processed_path):
-               output_files.append(processed_path)
-           if os.path.exists(analysis_path):
-               output_files.append(analysis_path)
-       
-       return_files = [f for f in output_files if os.path.exists(f)]
-       
-       log_output = result.stdout + "\n" + result.stderr
-       
-       return log_output, return_files
-   
-   with gr.Blocks() as demo:
-       gr.Markdown("# Crypto Tweet Processor")
-       gr.Markdown("Upload CSV files containing tweet data to process")
-       
-       with gr.Row():
-           files_input = gr.File(file_count="multiple", label="Upload CSV Files")
-           reset_checkbox = gr.Checkbox(label="Reset Processing", value=False)
-       
-       process_btn = gr.Button("Process Tweets")
-       
-       output_text = gr.Textbox(label="Processing Log")
-       output_files = gr.File(label="Processed Files", file_count="multiple")
-       
-       process_btn.click(
-           process_tweets,
-           inputs=[files_input, reset_checkbox],
-           outputs=[output_text, output_files]
-       )
-   
-   # Add the modified processing script code here
-   with open("process_tweet_huggingface.py", "w") as f:
-       f.write(#!/usr/bin/env python3
+
+# Install spaCy model
+os.system("python -m spacy download en_core_web_sm")
+
+def process_tweets(files, reset_processing=False):
+# Save uploaded files
+file_paths = []
+for file in files:
+    if file.name.endswith('.csv'):
+        # Ensure directory exists
+        os.makedirs("projects_twitter_post", exist_ok=True)
+        
+        # Save file to the directory
+        dest_path = f"projects_twitter_post/{os.path.basename(file.name)}"
+        os.system(f"cp {file.name} {dest_path}")
+        file_paths.append(dest_path)
+
+if not file_paths:
+    return "No CSV files uploaded. Please upload CSV files containing tweet data."
+
+# Run the processing script
+reset_flag = "--reset" if reset_processing else ""
+result = subprocess.run(
+    f"python process_tweet_huggingface.py {reset_flag}",
+    shell=True,
+    capture_output=True,
+    text=True
+)
+
+# Check if output files were created
+output_files = []
+for file_path in file_paths:
+    base_name = os.path.basename(file_path).replace('.csv', '')
+    processed_path = f"projects_twitter_post/{base_name}_processed.csv"
+    analysis_path = f"projects_twitter_post/{base_name}_analysis.csv"
+    
+    if os.path.exists(processed_path):
+        output_files.append(processed_path)
+    if os.path.exists(analysis_path):
+        output_files.append(analysis_path)
+
+return_files = [f for f in output_files if os.path.exists(f)]
+
+log_output = result.stdout + "\n" + result.stderr
+
+return log_output, return_files
+
+with gr.Blocks() as demo:
+gr.Markdown("# Crypto Tweet Processor")
+gr.Markdown("Upload CSV files containing tweet data to process")
+
+with gr.Row():
+    files_input = gr.File(file_count="multiple", label="Upload CSV Files")
+    reset_checkbox = gr.Checkbox(label="Reset Processing", value=False)
+
+process_btn = gr.Button("Process Tweets")
+
+output_text = gr.Textbox(label="Processing Log")
+output_files = gr.File(label="Processed Files", file_count="multiple")
+
+process_btn.click(
+    process_tweets,
+    inputs=[files_input, reset_checkbox],
+    outputs=[output_text, output_files]
+)
+
+# Add the modified processing script code here
+with open("process_tweet_huggingface.py", "w") as f:
+f.write(#!/usr/bin/env python3
 """
 Tweet Processing Script for Google Colab - Enhanced with NLP and Sentiment Analysis
 This version is optimized for Google Colab with GPU acceleration and Google Drive integration.
@@ -882,5 +882,5 @@ if __name__ == "__main__":
     
     # Run the main function
     main(reset_checkpoint=reset_checkpoint, input_file=input_file) )
-   
-   demo.launch()
+
+demo.launch()