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from transformers import pipeline
DEVICE = -1
models = {}
def get_model(task_name):
if task_name not in models:
if task_name == "Chatbot":
models[task_name] = pipeline(
"text-generation",
model="microsoft/DialoGPT-small",
device=DEVICE
)
elif task_name == "Sentiment Analysis":
models[task_name] = pipeline(
"sentiment-analysis",
model="distilbert-base-uncased-finetuned-sst-2-english",
device=DEVICE
)
elif task_name == "NER":
models[task_name] = pipeline(
"token-classification",
model="dslim/bert-base-NER",
aggregation_strategy="simple",
device=DEVICE
)
elif task_name == "Summarization":
models[task_name] = pipeline(
"summarization",
model="sshleifer/distilbart-cnn-12-6",
device=DEVICE
)
elif task_name == "Translation (EN→FR)":
models[task_name] = pipeline(
"translation_en_to_fr",
model="Helsinki-NLP/opus-mt-en-fr",
device=DEVICE
)
elif task_name == "Fill Mask":
models[task_name] = pipeline(
"fill-mask",
model="bert-base-uncased",
device=DEVICE
)
return models[task_name]
def run_task(task, user_input, chat_history):
if not user_input.strip():
return "Please enter some text.", chat_history
model = get_model(task)
if task == "Chatbot":
response = model(
user_input,
max_new_tokens=100,
do_sample=True,
temperature=0.7
)
generated_text = response[0]["generated_text"]
bot_reply = generated_text[len(user_input):].strip()
chat_history = chat_history + [
{"role": "user", "content": user_input},
{"role": "assistant", "content": bot_reply}
]
return "", chat_history
elif task == "Sentiment Analysis":
sentiment = model(user_input)[0]
result = f"Label: {sentiment['label']}\nConfidence: {sentiment['score']:.2f}"
return result, chat_history
elif task == "Summarization":
summary = model(
user_input,
max_length=120,
min_length=40,
do_sample=False
)[0]["summary_text"]
return summary, chat_history
elif task == "NER":
entities = model(user_input)
if not entities:
return "No entities found.", chat_history
formatted = "\n".join(
f"{e['word']} ({e['entity_group']}) - {e['score']:.2f}"
for e in entities
)
return formatted, chat_history
elif task == "Translation (EN→FR)":
translation = model(user_input)[0]["translation_text"]
return translation, chat_history
elif task == "Fill Mask":
if "<mask>" not in user_input:
return "Please include the token <mask> in your sentence.", chat_history
predictions = model(user_input)
formatted = "\n".join(
f"{p['token_str']} (score: {p['score']:.4f})"
for p in predictions
)
return formatted, chat_history
with gr.Blocks(title="NLP Application") as demo:
gr.Markdown("# NLP Application")
task_dropdown = gr.Dropdown(
choices=[
"Chatbot",
"Sentiment Analysis",
"NER",
"Summarization",
"Translation (EN→FR)",
"Fill Mask"
],
label="Select NLP Task"
)
user_input = gr.Textbox(
lines=5,
placeholder="Enter text here...",
label="Input Text"
)
output_box = gr.Textbox(label="Output")
chatbot = gr.Chatbot(label="Conversation")
state = gr.State([])
run_button = gr.Button("Run")
run_button.click(
fn=run_task,
inputs=[task_dropdown, user_input, state],
outputs=[output_box, chatbot]
)
if __name__ == "__main__":
demo.launch()
|