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added step 1-6 from semantic search
Verified signature# Step 1 - Semantic Search from sentence_transformers import SentenceTransformer import torch # Step 2 - Semantic Search # Open the water_cycle.txt file in read mode with UTF-8 encoding with open("water_cycle.txt", "r", encoding="utf-8") as file: # Read the entire contents of the file and store it in a variable water_cycle_text = file.read() # Print the text below print(water_cycle_text) # Step 3 - Semantic Search def preprocess_text(text): # Strip extra whitespace from the beginning and the end of the text cleaned_text = text.strip() # Split the cleaned_text by every newline character (\n) chunks = cleaned_text.split("\n") # Create an empty list to store cleaned chunks cleaned_chunks = [] # Write your for-in loop below to clean each chunk and add it to the cleaned_chunks list for chunk in chunks: stripped_chunk = chunk.strip() if len(stripped_chunk) > 0: cleaned_chunks.append(stripped_chunk) # Print cleaned_chunks print(clean…37366f3 -
adding api (#1)
Verified signature- adding api (e2ad74702bc37b21c3d97f5fdcfe32e2784c262b) Co-authored-by: Aashna Jannu <aashnaj@users.noreply.huggingface.co>2c58982
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initial commit
Verified signature3d49193 -
Duplicate from gradio-templates/chatbot
Verified signatureCo-authored-by: Pete <pngwn@users.noreply.huggingface.co>6a6b6d0
