Update app.py

app.py

@@ -1,8 +1,11 @@
 import streamlit as st
 from transformers import pipeline
+from langdetect import detect
 from datetime import datetime
 import base64
 import os
+import PyPDF2
+from docx import Document
 
 # Initialize models
 summarizer = pipeline("summarization")
@@ -10,21 +13,20 @@ translator_en_ur = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ur")
 translator_ur_en = pipeline("translation", model="Helsinki-NLP/opus-mt-ur-en")
 grammar_corrector = pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")
 
-# Set up page config
 st.set_page_config(page_title="CSS AI Assistant by Komal", layout="wide")
 
 st.title("📚 CSS AI Assistant 🇵🇰")
 st.write("Helping students with news summaries, precis, essays & more — for free!")
 
-# Sidebar Menu
 menu = st.sidebar.radio("Select Feature", [
     "Daily News Summary",
     "Precis Evaluation",
     "Essay Feedback",
-    "Saved Notes"
+    "Saved Notes",
+    "File Upload"
 ])
 
-# File to save notes
+# Local file to save notes
 NOTES_FILE = "saved_notes.txt"
 
 def save_note(title, content):
@@ -41,14 +43,28 @@ def generate_download_link(text, filename):
     b64 = base64.b64encode(text.encode()).decode()
     return f'<a href="data:file/txt;base64,{b64}" download="{filename}">📥 Download Notes</a>'
 
+def extract_text_from_pdf(uploaded_file):
+    pdf_reader = PyPDF2.PdfReader(uploaded_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+def extract_text_from_word(uploaded_file):
+    doc = Document(uploaded_file)
+    text = ""
+    for para in doc.paragraphs:
+        text += para.text
+    return text
+
 # --- Daily News Summary ---
 if menu == "Daily News Summary":
     st.header("📰 AI-Powered News Summarizer")
-    user_news = st.text_area("Paste or write today's news:", height=200)
+    user_news = st.text_area("Paste or write today's news:", height=300)
     lang = st.radio("Language", ["English", "Urdu"])
 
     if st.button("Summarize") and user_news:
-        summary = summarizer(user_news, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
+        summary = summarizer(user_news, max_length=500, min_length=100, do_sample=False)[0]['summary_text']
         if lang == "Urdu":
             translated = translator_en_ur(summary)[0]['translation_text']
             st.success("Summary in Urdu:")
@@ -64,7 +80,7 @@ if menu == "Daily News Summary":
 # --- Precis Evaluation ---
 elif menu == "Precis Evaluation":
     st.header("✍️ Precis Evaluation Tool")
-    precis_input = st.text_area("Enter your precis for evaluation:", height=200)
+    precis_input = st.text_area("Enter your precis for evaluation:", height=300)
     if st.button("Evaluate Precis") and precis_input:
         corrected = grammar_corrector("grammar: " + precis_input)[0]['generated_text']
         st.success("✅ Corrected Precis:")
@@ -75,7 +91,7 @@ elif menu == "Precis Evaluation":
 # --- Essay Feedback ---
 elif menu == "Essay Feedback":
     st.header("🖋️ Essay Evaluation Tool")
-    essay_input = st.text_area("Paste your essay here:", height=300)
+    essay_input = st.text_area("Paste your essay here:", height=400)
     if st.button("Evaluate Essay") and essay_input:
         corrected = grammar_corrector("grammar: " + essay_input)[0]['generated_text']
         st.success("✅ Suggestions:")
@@ -89,3 +105,29 @@ elif menu == "Saved Notes":
     notes = load_notes()
     st.markdown(notes)
     st.markdown(generate_download_link(notes, "css_notes.txt"), unsafe_allow_html=True)
+
+# --- File Upload ---
+elif menu == "File Upload":
+    st.header("📄 File Upload - PDF or Word")
+    uploaded_file = st.file_uploader("Upload a PDF or Word file", type=["pdf", "docx"])
+    
+    if uploaded_file is not None:
+        file_type = uploaded_file.type
+        if file_type == "application/pdf":
+            file_text = extract_text_from_pdf(uploaded_file)
+        elif file_type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
+            file_text = extract_text_from_word(uploaded_file)
+
+        st.text_area("Extracted Text", file_text, height=300)
+
+        # Now you can apply the summarization, translation, or grammar correction on the extracted text
+        lang = st.radio("Language for Summary/Translation", ["English", "Urdu"])
+        if st.button("Summarize and Translate") and file_text:
+            summary = summarizer(file_text, max_length=500, min_length=100, do_sample=False)[0]['summary_text']
+            if lang == "Urdu":
+                translated = translator_en_ur(summary)[0]['translation_text']
+                st.success("Summary in Urdu:")
+                st.write(translated)
+            else:
+                st.success("Summary in English:")
+                st.write(summary)