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app.py

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+import gradio as gr
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForTokenClassification
+import torch
+import pandas as pd
+
+# Load the models and tokenizers
+QA_model = AutoModelForQuestionAnswering.from_pretrained("guldasta/xtreme-MLQA.hi.hi")
+QA_token = AutoTokenizer.from_pretrained("guldasta/xtreme-MLQA.hi.hi")
+
+Translation_model = AutoModelForSeq2SeqLM.from_pretrained("guldasta/Helsinki-NLP-opus-mt-mul-en-finetuned-hi-to-en")
+Translation_token = AutoTokenizer.from_pretrained("guldasta/Helsinki-NLP-opus-mt-mul-en-finetuned-hi-to-en")
+
+TokenClass_Model = AutoModelForTokenClassification.from_pretrained("guldasta/roberta-ner-multilingua-HiNER-Org")
+TokenClass_Token = AutoTokenizer.from_pretrained("guldasta/roberta-ner-multilingua-HiNER-Org")
+
+# Define the functions
+def get_ans(question, context):
+    model_inputs = QA_token(question, context, return_tensors="pt")
+    outputs = QA_model(**model_inputs)
+    start_logits = outputs.start_logits
+    end_logits = outputs.end_logits
+    start_indx = torch.argmax(start_logits)
+    end_indx = torch.argmax(end_logits)
+    answer = QA_token.decode(model_inputs['input_ids'][0][start_indx:end_indx + 1])
+    return answer
+
+def translate_text(text):
+    model_inputs = Translation_token(text, return_tensors="pt")
+    outputs = Translation_model.generate(**model_inputs)
+    translated_text = Translation_token.decode(outputs[0], skip_special_tokens=True)
+    return translated_text
+
+def get_pos(text):
+    inputs = TokenClass_Token(text, return_tensors="pt")
+    outputs = TokenClass_Model(**inputs)
+    logits = outputs.logits
+    predictions = torch.argmax(logits, dim=2)
+    tokens = TokenClass_Token.convert_ids_to_tokens(inputs["input_ids"][0])
+    predicted_labels = [TokenClass_Model.config.id2label[label.item()] for label in predictions[0]]
+
+    filtered_tokens = []
+    filtered_labels = []
+    for token, label in zip(tokens, predicted_labels):
+        if token not in TokenClass_Token.all_special_tokens:
+            filtered_tokens.append(token)
+            filtered_labels.append(label)
+
+    df = pd.DataFrame({
+        "Token": filtered_tokens,
+        "Entity": filtered_labels
+    })
+    return df
+
+# Create Gradio interfaces
+qa_interface = gr.Interface(
+    fn=get_ans,
+    inputs=[
+        gr.Textbox(lines=5, placeholder="Type a paragraph or context here...", label="Paragraph"),
+        gr.Textbox(lines=1, placeholder="Type your question here...", label="Question"),
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    title="Question Answering",
+    description="Enter a paragraph and ask a question to get the answer."
+)
+
+translation_interface = gr.Interface(
+    fn=translate_text,
+    inputs=gr.Textbox(lines=2, placeholder="Type your text here...", label="Text to Translate"),
+    outputs=gr.Textbox(label="Translated Text"),
+    title="Text Translation",
+    description="Enter text to translate from Hindi to English."
+)
+
+pos_tag_interface = gr.Interface(
+    fn=get_pos,
+    inputs=gr.Textbox(lines=2, placeholder="Type your text here...", label="Text for POS Tagging"),
+    outputs=gr.Dataframe(headers=["Token", "Entity"]),
+    title="POS Tagging",
+    description="Enter text to get POS tags and named entities."
+)
+
+# Combine all interfaces into a single Gradio app
+app = gr.TabbedInterface(
+    [qa_interface, translation_interface, pos_tag_interface],
+    ["Question Answering", "Translation", "POS Tagging"]
+)
+
+# Launch the app
+app.launch()
+
+
+
+

requirements.txt

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+gradio
+transformers
+torch
+pandas