Initial commit

.gradio/certificate.pem

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+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV
+HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq
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+mRGunUHBcnWEvgJBQl9nJEiU0Zsnvgc/ubhPgXRR4Xq37Z0j4r7g1SgEEzwxA57d
+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/

.idea/PT.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.13 (PT)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/PT.iml" filepath="$PROJECT_DIR$/.idea/PT.iml" />
+    </modules>
+  </component>
+</project>

PT.py

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+import torch
+import gradio as gr
+import json
+from transformers import pipeline, AutoTokenizer
+
+print("Loading Summarization model: t5-base...")
+text_summary = pipeline(
+    "summarization",
+    model="t5-base",
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
+)
+print("Summarization model loaded.")
+
+def summarize_text(input_text):
+    output = text_summary(input_text, min_length=30, max_length=120)
+    return output[0]['summary_text']
+
+print("Loading Translation model: facebook/nllb-200-distilled-600M...")
+text_translator = pipeline(
+    "translation",
+    model="facebook/nllb-200-distilled-600M",
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
+)
+print("Translation model loaded.")
+
+with open('language.json', 'r', encoding='utf-8') as file:
+    language_data = json.load(file)
+
+def get_FLORES_code_from_language(language_name):
+    for entry in language_data:
+        if entry['Language'].lower() == language_name.lower():
+            return entry['FLORES-200 code']
+    return None
+
+def translate_text(text, destination_language_name):
+    dest_code = get_FLORES_code_from_language(destination_language_name)
+
+    if not dest_code:
+        return f"Error: FLORES-200 code not found for '{destination_language_name}'. Please select a language from the list."
+
+    print(f"Translating from English (eng_Latn) to {destination_language_name} ({dest_code})...")
+
+    translation = text_translator(
+        text,
+        src_lang="eng_Latn",
+        tgt_lang=dest_code
+    )
+    return translation[0]["translation_text"]
+
+print("Loading Question-Answering model: distilbert-base-uncased-distilled-squad...")
+Youtube = pipeline(
+    "question-answering",
+    model="distilbert-base-uncased-distilled-squad",
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
+)
+print("Question-Answering model loaded.")
+
+def read_file_content(file_obj):
+    try:
+        with open(file_obj.name, 'r', encoding='utf-8') as file:
+            return file.read()
+    except UnicodeDecodeError:
+        try:
+            with open(file_obj.name, 'r', encoding='latin-1') as file:
+                return file.read()
+        except Exception as e:
+            return f"An error occurred: {e}"
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+def get_answer(file, question):
+    context = read_file_content(file)
+    if "An error occurred" in context:
+        return context
+    answer = Youtube(question=question, context=context)
+    if answer['score'] < 0.2:
+        return "I am not confident I can answer this question based on the provided text."
+    return answer["answer"]
+
+def landing_page():
+    return (
+        "<div style='max-width:900px; margin:36px auto 0 auto; background: linear-gradient(135deg, #eef6fb 0%, #e0ecfa 100%); "
+        "border-radius: 30px; box-shadow:0 8px 36px #b6c4e033; padding: 60px 0 45px 0; text-align:center;'>"
+        "<h1 style='font-size:3.2em; color:#174ea6; font-weight:900; letter-spacing:-1.5px; margin-bottom:10px;'>Unified NLP Toolkit ✨</h1>"
+        "<p style='font-size:1.27em; color:#51627a; margin:24px 0 27px 0;'>"
+        "Summarize, translate, or ask questions on documents using a modern, friendly interface.<br><br>"
+        "Select a tool below to get started."
+        "</p>"
+        "<div style='display:flex; flex-wrap:wrap; justify-content:center; gap:30px; margin-top:27px;'>"
+        "<a href='#summarizer' style='background:linear-gradient(100deg,#2563eb 70%,#4f92fc 100%) !important; "
+        "color:white; padding:17px 36px 17px 36px; border-radius:16px; text-decoration:none; font-size:1.16em; font-weight:700; "
+        "transition: box-shadow .15s; box-shadow:0 4px 14px #2563eb22; outline:none;'>Text Summarizer</a>"
+        "<a href='#translator' style='background:linear-gradient(100deg,#059669 70%,#34d399 110%) !important; "
+        "color:white; padding:17px 36px 17px 36px; border-radius:16px; text-decoration:none; font-size:1.16em; font-weight:700; "
+        "transition: box-shadow .15s; box-shadow:0 4px 14px #05966922; outline:none;'>Translator</a>"
+        "<a href='#qna' style='background:linear-gradient(100deg,#f59e42 70%,#fbbf24 110%) !important; "
+        "color:white; padding:17px 36px 17px 36px; border-radius:16px; text-decoration:none; font-size:1.16em; font-weight:700; "
+        "transition: box-shadow .15s; box-shadow:0 4px 14px #f59e4222; outline:none;'>Document Q&A</a>"
+        "</div>"
+        "</div>"
+    )
+
+def footer():
+    return (
+        "<div style='text-align:center; margin-top:54px; margin-bottom:12px; color:#64748b; font-size:1.13em; letter-spacing:0.5px;'>"
+        "Made with <span style='color:#e11d48;'>&#10084;&#65039;</span> by tushar"
+        "</div>"
+    )
+
+with gr.Blocks(title="Unified NLP Toolkit ✨", theme=gr.themes.Soft(primary_hue="blue", neutral_hue="slate")) as demo:
+    gr.HTML(landing_page())
+
+    with gr.Tab("Text Summarizer", elem_id="summarizer"):
+        gr.Markdown("### Text Summarizer")
+        gr.Markdown("> Enter your text below and click **Summarize** to generate a concise summary.")
+        inp = gr.Textbox(
+            label="Input",
+            lines=10,
+            placeholder="Paste your text here..."
+        )
+        btn = gr.Button(
+            "Summarize",
+            elem_id="summarize-btn",
+            variant="secondary"
+        )
+        out = gr.Textbox(label="Summary", lines=4)
+        btn.click(fn=summarize_text, inputs=inp, outputs=out)
+
+    with gr.Tab("Multilanguage Translator", elem_id="translator"):
+        gr.Markdown("### Multilanguage Translator")
+        gr.Markdown("> Enter English text, select an Indian language, and click **Translate**.")
+        inp3 = gr.Textbox(
+            label="English Text",
+            lines=10
+        )
+        lang_options = [entry['Language'] for entry in language_data]
+        lang_dropdown = gr.Dropdown(
+            lang_options,
+            label="Select Indian Language"
+        )
+        btn3 = gr.Button(
+            "Translate",
+            elem_id="translate-btn",
+            variant="secondary"
+        )
+        out3 = gr.Textbox(label="Translated Text", lines=4)
+        btn3.click(fn=translate_text, inputs=[inp3, lang_dropdown], outputs=out3)
+
+    with gr.Tab("Document QnA", elem_id="qna"):
+        gr.Markdown("### Document Q&A")
+        gr.Markdown("> Upload a `.txt` file and ask a question about its content.")
+        file_inp = gr.File(label="Upload Text File")
+        q_inp = gr.Textbox(label="Your Question", lines=2)
+        btn4 = gr.Button(
+            "Get Answer",
+            elem_id="qna-btn",
+            variant="secondary"
+        )
+        out4 = gr.Textbox(label="Answer", lines=2)
+        btn4.click(fn=get_answer, inputs=[file_inp, q_inp], outputs=out4)
+
+    gr.HTML(footer())
+
+demo.launch()

Requirements.txt.txt

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+torch
+gradio
+transformers

language.json

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+[
+    {"Language": "Assamese", "FLORES-200 code": "asm_Beng"},
+    {"Language": "Awadhi", "FLORES-200 code": "awa_Deva"},
+    {"Language": "Bengali", "FLORES-200 code": "ben_Beng"},
+    {"Language": "Bhojpuri", "FLORES-200 code": "bho_Deva"},
+    {"Language": "Dogri", "FLORES-200 code": "dgo_Deva"},
+    {"Language": "Gujarati", "FLORES-200 code": "guj_Gujr"},
+    {"Language": "Hindi", "FLORES-200 code": "hin_Deva"},
+    {"Language": "Kannada", "FLORES-200 code": "kan_Knda"},
+    {"Language": "Kashmiri", "FLORES-200 code": "kas_Arab"},
+    {"Language": "Konkani", "FLORES-200 code": "kok_Deva"},
+    {"Language": "Maithili", "FLORES-200 code": "mai_Deva"},
+    {"Language": "Malayalam", "FLORES-200 code": "mal_Mlym"},
+    {"Language": "Marathi", "FLORES-200 code": "mar_Deva"},
+    {"Language": "Meitei (Bengali)", "FLORES-200 code": "mni_Beng"},
+    {"Language": "Mizo", "FLORES-200 code": "lus_Latn"},
+    {"Language": "Nepali", "FLORES-200 code": "npi_Deva"},
+    {"Language": "Oriya", "FLORES-200 code": "ory_Orya"},
+    {"Language": "Punjabi (Gurmukhi)", "FLORES-200 code": "pan_Guru"},
+    {"Language": "Sanskrit", "FLORES-200 code": "san_Deva"},
+    {"Language": "Santali", "FLORES-200 code": "sat_Olck"},
+    {"Language": "Sindhi", "FLORES-200 code": "snd_Arab"},
+    {"Language": "Tamil", "FLORES-200 code": "tam_Taml"},
+    {"Language": "Telugu", "FLORES-200 code": "tel_Telu"},
+    {"Language": "Urdu", "FLORES-200 code": "urd_Arab"}
+]