app.py

import gradio as gr
import json
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
from transformers import AutoModelForTokenClassification
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification


# Define hyperparameters for QnA
learning_rate = 3e-5
batch_size = 16
epochs = 3
max_seq_length = 512
warmup_steps = 100
weight_decay = 0.01
dropout_prob = 0.1
gradient_clip_value = 1.0
q_n_a_model_name = "deepset/roberta-base-squad2"
q_n_a_model = AutoModelForQuestionAnswering.from_pretrained(q_n_a_model_name)
q_n_a_tokenizer = AutoTokenizer.from_pretrained(q_n_a_model_name)  


classification_tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
classification_model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")



context = gr.Textbox(label="Add the Context (Paragraph or texts) for which you want to get insights", lines=10, outputs="text")

def q_n_a_fn(context, text):
    QA_input = {'question': text, 'context': context}
    
    # Set the device (CPU or GPU)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    q_n_a_model.to(device)
    
    # Convert inputs to tensors
    inputs = q_n_a_tokenizer(QA_input["context"], QA_input["question"], return_tensors="pt", max_length=max_seq_length, truncation=True, padding="max_length").to(device)

    # Get predictions
    with torch.no_grad():
        outputs = q_n_a_model(**inputs)  # Use q_n_a_model to get model predictions
    
    # Get the predicted answer span indices
    start_idx, end_idx = torch.argmax(outputs.start_logits), torch.argmax(outputs.end_logits)

    # Ensure indices are within bounds
    start_idx = min(start_idx, len(inputs["input_ids"][0]) - 1)
    end_idx = min(end_idx, len(inputs["input_ids"][0]) - 1)

    # Find the answer tokens in the input
    answer_tokens = inputs["input_ids"][0][start_idx : end_idx + 1]

    # Decode the answer tokens into a human-readable answer
    answer = q_n_a_tokenizer.decode(inputs["input_ids"][0][start_idx:end_idx+1], skip_special_tokens=True)

    return answer

def classification_fn(context):    
    inputs = classification_tokenizer(context, return_tensors="pt")
    with torch.no_grad():
        logits = classification_model(**inputs).logits
        class_probabilities = torch.softmax(logits, dim=1)

    class_probabilities = torch.softmax(logits, dim=1)
    class_probabilities = class_probabilities[0].tolist()  # Convert to a Python list
    return {"POSITIVE": class_probabilities[0], "NEGATIVE": class_probabilities[1]}

def translate_fn(context, text):
    return context
    
with gr.Blocks(theme='gradio/soft') as demo:
    gr.Markdown("<h1>Basic NLP Operations</h1>")
    gr.Markdown("Bringing basic NLP operations together.")
    with gr.Tab("Question and Answer"):
        with gr.Row():
            gr.Interface(fn=q_n_a_fn, inputs=[context, gr.Textbox(label="Ask question", lines=1)], outputs="text")
    
    with gr.Tab("Classifier"):
        with gr.Row():
            gr.Interface(fn=classification_fn, inputs=[context], outputs=[gr.Label()])
    
    with gr.Tab("Translation"):
        with gr.Row():
            gr.Interface(fn=translate_fn, inputs=[context, gr.Radio(["French", "Hindi", "Spanish"], label="Languages", info="Select language")], outputs="text")

    with gr.Tab("Summarization"):
        with gr.Row():
            gr.Interface(fn=classification_fn, inputs=[context], outputs="label")

    with gr.Tab("Text To Speech"):
        with gr.Row():
            gr.Interface(fn=classification_fn, inputs=[context], outputs="audio")

    with gr.Tab("Text To Text"):
        with gr.Row():
            gr.Interface(fn=classification_fn, inputs=[context], outputs="text")

if __name__ == "__main__":
    demo.launch()