email upload

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 
-from mailparser import parse_from_string
+from mailparser import parse_from_file
 from bs4 import BeautifulSoup
 from gliner import GLiNER
 from typing import Dict, Union, List
@@ -11,12 +11,11 @@ import os
 import en_core_web_sm
 nlp = en_core_web_sm.load()
 
-# nlp = spacy.load("en_core_web_sm")
 _MODEL = {}
 _CACHE_DIR = os.environ.get("CACHE_DIR", None)
 
-def accept_mail(email_content):
-    email = parse_from_string(email_content)
+def accept_mail(file_path):
+    email = parse_from_file(file_path)
     return email
 
 def clean_email(email):
@@ -64,8 +63,8 @@ def parse_query(sentences: List[str], labels: List[str], threshold: float = 0.3,
 
     return results
 
-def present(email_content, labels, multilingual=False):
-    email = accept_mail(email_content)
+def present(email_file, labels, multilingual=False):
+    email = accept_mail(email_file)
     cleaned_text = clean_email(email)
     further_cleaned_text = remove_special_characters(cleaned_text)
     sentence_list = get_sentences(further_cleaned_text)
@@ -87,7 +86,7 @@ labels = ["PERSON", "PRODUCT", "DEAL", "ORDER", "ORDER PAYMENT METHOD", "STORE",
 demo = gr.Interface(
     fn=present, 
     inputs=[
-        gr.components.Textbox(label="Email Content"),
+        gr.components.File(label="Upload Email (.eml file)"),
         gr.components.CheckboxGroup(
             choices=labels,
             label="Labels to Detect",
@@ -103,7 +102,7 @@ demo = gr.Interface(
         gr.components.Textbox(label="Cleaned Body"),
         gr.components.JSON(label="Extracted Entities")
     ],
-    title="Email Info",
-    description="Enter the email content below to view its details and detected entities."
+    title="Email Info Extractor",
+    description="Upload an email file (.eml) to extract its details and detected entities."
 )
 demo.launch(share=True)