Create app.py

app.py

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+# Install necessary packages
+!pip install transformers pandas google-colab
+
+import pandas as pd
+from transformers import pipeline
+from google.colab import files
+
+# Function to enhance title using Hugging Face model
+def generate_title_with_huggingface(description):
+    result = generator(description, max_length=100, num_return_sequences=1)
+    return result[0]['generated_text']
+
+# Upload the CSV file
+uploaded = files.upload()
+
+# Assuming the file name is 'output.csv' after upload
+file_name = list(uploaded.keys())[0]
+
+# Load the CSV file
+df = pd.read_csv(file_name)
+
+# Initialize the text generation model (e.g., GPT-2)
+generator = pipeline('text-generation', model='gpt-2')
+
+# Apply the function to generate SEO-friendly titles
+df['Title'] = df['Description'].apply(generate_title_with_huggingface)
+
+# Function to generate keywords (basic example)
+def generate_keywords(description):
+    words = set(description.replace(',', '').replace('.', '').split())
+    return ",".join(list(words)[:50])
+
+# Generate basic keywords from the description (you can enhance this further)
+df['Keywords'] = df['Description'].apply(generate_keywords)
+
+# Save the SEO-optimized DataFrame to a new CSV file
+seo_output_file_path = 'seo_huggingface_filename_title_keywords.csv'
+df[['Filename', 'Title', 'Keywords']].to_csv(seo_output_file_path, index=False)
+
+# Download the resulting CSV file
+files.download(seo_output_file_path)