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import os
from entity_recognition import extract_entities
from entity_recognition import generate_word_cloud
from pydantic import BaseModel
# Define paths
TEXT_FOLDER = "jfk_text"
SUMMARY_FOLDER = "summaryoutput"
# Request model
class TextRequest(BaseModel):
text: str
def list_files():
"""List all Markdown (.md) files in the 'jfk_text' folder."""
if os.path.exists(TEXT_FOLDER):
return [f for f in os.listdir(TEXT_FOLDER) if f.endswith(".md")]
return []
def read_file(file_path):
"""Read the content of a given file."""
with open(file_path, "r", encoding="utf-8") as file:
return file.read()
def get_summary(file_name):
"""Get the summary of a file if it exists."""
summary_file = f"summary_{file_name}"
summary_path = os.path.join(SUMMARY_FOLDER, summary_file)
if os.path.exists(summary_path):
return read_file(summary_path)
return "Summary not found."
def process_file(file_name):
"""Process a file to generate summary, entities, and word cloud."""
file_path = os.path.join(TEXT_FOLDER, file_name)
if not os.path.exists(file_path):
return "Error: File not found", {}, None
# Read content
text = read_file(file_path)
# Get precomputed summary
summary = get_summary(file_name)
# Extract entities
entities = {"entities": extract_entities(text)}
wordcloud_path = os.path.join("wordcloud_output", f"wordcloud_{file_name}.png") # Ensure a valid file path
os.makedirs("wordcloud_output", exist_ok=True) # Ensure output directory exists
generate_word_cloud(text,wordcloud_path) # Correct function call
return summary, entities, wordcloud_path
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