Create app.py

app.py

@@ -0,0 +1,132 @@
+import streamlit as st
+import fitz
+from transformers import pipeline, MBart50TokenizerFast, MBartForConditionalGeneration
+from multiprocessing import Pool, cpu_count
+import tempfile
+
+# Load summarization pipeline
+summarizer = pipeline("summarization", model="Falconsai/text_summarization")
+
+# Load translation model and tokenizer
+model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt")
+tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-one-to-many-mmt", src_lang="en_XX")
+
+# Define max chunk length
+max_chunk_length = 1024
+
+# Function to chunk text
+def chunk_text(text, max_chunk_length):
+    chunks = []
+    current_chunk = ""
+    for sentence in text.split("."):
+        if len(current_chunk) + len(sentence) + 1 <= max_chunk_length:
+            if current_chunk != "":
+                current_chunk += " "
+            current_chunk += sentence.strip()
+        else:
+            chunks.append(current_chunk)
+            current_chunk = sentence.strip()
+    if current_chunk != "":
+        chunks.append(current_chunk)
+    return chunks
+
+# Function to summarize and translate a chunk
+def summarize_and_translate_chunk(chunk, lang):
+    summary = summarizer(chunk, max_length=150, min_length=30, do_sample=False)
+    summary_text = summary[0]['summary_text']
+
+    # Translate summary
+    translated_chunk = translate_summary(summary_text, lang)
+    return translated_chunk
+
+# Function to translate the summary
+def translate_summary(summary, lang):
+    # Chunk text if it exceeds maximum length
+    if len(summary) > max_chunk_length:
+        chunks = chunk_text(summary, max_chunk_length)
+    else:
+        chunks = [summary]
+
+    # Translate each chunk
+    translated_chunks = []
+    for chunk in chunks:
+        inputs = tokenizer(chunk, return_tensors="pt", padding=True, truncation=True)
+        generated_tokens = model.generate(
+            **inputs,
+            forced_bos_token_id=tokenizer.lang_code_to_id[lang],
+            max_length=1024,
+            num_beams=4,
+            early_stopping=True,
+            length_penalty=2.0,
+        )
+        translated_chunks.append(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0])
+
+    return " ".join(translated_chunks)
+
+
+
+# Function to read PDF and summarize and translate chunk by chunk
+def summarize_and_translate_pdf(uploaded_file, lang):
+    # Save uploaded PDF to a temporary file
+    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+        temp_file.write(uploaded_file.read())
+        temp_file_path = temp_file.name
+
+    try:
+        doc = fitz.open(temp_file_path)
+    except FileNotFoundError:
+        st.error("File not found. Please make sure the file path is correct.")
+        return []
+
+    total_chunks = len(doc)
+    chunks = []
+
+    for i in range(total_chunks):
+        page = doc.load_page(i)
+        text = page.get_text()
+        chunks.extend([text[j:j+max_chunk_length] for j in range(0, len(text), max_chunk_length)])
+
+    # Use multiprocessing to parallelize the process
+    with Pool(cpu_count()) as pool:
+        translated_chunks = pool.starmap(summarize_and_translate_chunk, [(chunk, lang) for chunk in chunks])
+
+    # Delete temporary file
+    os.unlink(temp_file_path)
+
+    return translated_chunks
+
+
+# Streamlit UI
+st.title("PDF Summarization and Translation")
+
+# File upload
+uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
+if uploaded_file:
+    # Display uploaded file
+    st.write("Uploaded PDF file:", uploaded_file.name)
+
+    # Language selection
+    languages = {
+        "Arabic": "ar_AR", "Czech": "cs_CZ", "German": "de_DE", "English": "en_XX", "Spanish": "es_XX",
+        "Estonian": "et_EE", "Finnish": "fi_FI", "French": "fr_XX", "Gujarati": "gu_IN", "Hindi": "hi_IN",
+        "Italian": "it_IT", "Japanese": "ja_XX", "Kazakh": "kk_KZ", "Korean": "ko_KR", "Lithuanian": "lt_LT",
+        "Latvian": "lv_LV", "Burmese": "my_MM", "Nepali": "ne_NP", "Dutch": "nl_XX", "Romanian": "ro_RO",
+        "Russian": "ru_RU", "Sinhala": "si_LK", "Turkish": "tr_TR", "Vietnamese": "vi_VN", "Chinese": "zh_CN",
+        "Afrikaans": "af_ZA", "Azerbaijani": "az_AZ", "Bengali": "bn_IN", "Persian": "fa_IR", "Hebrew": "he_IL",
+        "Croatian": "hr_HR", "Indonesian": "id_ID", "Georgian": "ka_GE", "Khmer": "km_KH", "Macedonian": "mk_MK",
+        "Malayalam": "ml_IN", "Mongolian": "mn_MN", "Marathi": "mr_IN", "Polish": "pl_PL", "Pashto": "ps_AF",
+        "Portuguese": "pt_XX", "Swedish": "sv_SE", "Swahili": "sw_KE", "Tamil": "ta_IN", "Telugu": "te_IN",
+        "Thai": "th_TH", "Tagalog": "tl_XX", "Ukrainian": "uk_UA", "Urdu": "ur_PK", "Xhosa": "xh_ZA",
+        "Galician": "gl_ES", "Slovene": "sl_SI"
+    }
+
+    lang = st.selectbox("Select language for translation", list(languages.keys()))
+
+    # Translate PDF
+    if st.button("Summarize and Translate"):
+        translated_chunks = summarize_and_translate_pdf(uploaded_file, languages[lang])
+        
+        # Display translated text
+        st.header("Translated Summary")
+        for chunk in translated_chunks:
+            st.write(chunk)