Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+
+# Use CPU on Hugging Face Spaces free tier
+DEVICE = -1
+
+# Lazy loading (loads model only when first used)
+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
+            )
+
+    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,
+            pad_token_id=50256
+        )
+
+        bot_reply = response[0]["generated_text"]
+        chat_history = chat_history + [(user_input, 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
+
+
+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)"
+        ],
+        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]
+    )
+
+demo.launch()