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app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Initialize the sentiment analysis pipeline
+sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+# Initialize text generation pipeline
+generator = pipeline("text-generation", model="distilgpt2", max_length=100)
+
+def analyze_sentiment(text):
+    """Analyze the sentiment of the input text."""
+    if not text.strip():
+        return "Please enter some text to analyze."
+    
+    result = sentiment_pipeline(text)[0]
+    label = result['label']
+    score = result['score']
+    
+    return f"Sentiment: {label}\nConfidence: {score:.2%}"
+
+def generate_text(prompt):
+    """Generate text based on the input prompt."""
+    if not prompt.strip():
+        return "Please enter a prompt to generate text."
+    
+    result = generator(prompt, max_length=100, num_return_sequences=1)
+    return result[0]['generated_text']
+
+def chatbot(message, history):
+    """Simple chatbot function."""
+    if not message.strip():
+        return "Please say something!"
+    
+    # Simple response logic
+    message_lower = message.lower()
+    
+    if "hello" in message_lower or "hi" in message_lower:
+        return "Hello! How can I help you today?"
+    elif "how are you" in message_lower:
+        return "I'm doing great, thanks for asking! I'm an AI assistant ready to help."
+    elif "bye" in message_lower:
+        return "Goodbye! Have a great day!"
+    elif "sentiment" in message_lower:
+        return "I can analyze sentiment! Go to the Sentiment Analysis tab and enter some text."
+    else:
+        # Use the generator for other inputs
+        response = generator(message, max_length=50, num_return_sequences=1)
+        return response[0]['generated_text']
+
+# Create the Gradio interface with tabs
+with gr.Blocks(title="Basic AI Assistant") as demo:
+    gr.Markdown("# 🤖 Basic AI Assistant")
+    gr.Markdown("A simple AI-powered assistant with multiple capabilities!")
+    
+    with gr.Tabs():
+        with gr.TabItem("💬 Chat"):
+            gr.Markdown("Chat with the AI assistant!")
+            chat_interface = gr.ChatInterface(chatbot, type="messages")
+        
+        with gr.TabItem("😊 Sentiment Analysis"):
+            gr.Markdown("Analyze the sentiment of your text (positive or negative).")
+            with gr.Row():
+                sentiment_input = gr.Textbox(
+                    label="Enter text to analyze",
+                    placeholder="Type something like 'I love this product!'",
+                    lines=3
+                )
+                sentiment_output = gr.Textbox(label="Result", lines=2)
+            sentiment_btn = gr.Button("Analyze Sentiment", variant="primary")
+            sentiment_btn.click(analyze_sentiment, inputs=sentiment_input, outputs=sentiment_output)
+        
+        with gr.TabItem("✍️ Text Generation"):
+            gr.Markdown("Generate text based on a prompt!")
+            with gr.Row():
+                gen_input = gr.Textbox(
+                    label="Enter your prompt",
+                    placeholder="Once upon a time...",
+                    lines=3
+                )
+                gen_output = gr.Textbox(label="Generated Text", lines=5)
+            gen_btn = gr.Button("Generate Text", variant="primary")
+            gen_btn.click(generate_text, inputs=gen_input, outputs=gen_output)
+    
+    gr.Markdown("---")
+    gr.Markdown("Made with ❤️ using Gradio and Hugging Face Transformers")
+
+if __name__ == "__main__":
+    demo.launch()