Update app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the Hugging Face model for text tasks
+text_generator = pipeline("text2text-generation", model="google/flan-t5-small")
+
+# --- Helper functions ---
+def summarize_text(text):
+    prompt = f"Summarize in 3 sentences: {text}"
+    result = text_generator(prompt, max_length=100, do_sample=False)
+    return result[0]['generated_text']
+
+def answer_question(topic, question):
+    prompt = f"Topic: {topic}\nAnswer this question: {question}"
+    result = text_generator(prompt, max_length=150, do_sample=False)
+    return result[0]['generated_text']
+
+def generate_story(theme):
+    prompt = f"Write a 200-word story about: {theme}"
+    result = text_generator(prompt, max_length=300, do_sample=True)
+    return result[0]['generated_text']
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Mini AI Text Assistant")
+    
+    task = gr.Radio(["Summarizer", "Q&A Bot", "Story Generator"], label="Choose a task")
+    
+    input_text = gr.Textbox(label="Enter text or topic/theme", placeholder="Type here...")
+    input_question = gr.Textbox(label="Enter question (for Q&A only)", visible=False)
+    output = gr.Textbox(label="Output")
+    
+    def process(task_choice, text, question):
+        if task_choice == "Summarizer":
+            return summarize_text(text)
+        elif task_choice == "Q&A Bot":
+            return answer_question(text, question)
+        elif task_choice == "Story Generator":
+            return generate_story(text)
+
+    task.change(lambda t: gr.update(visible=(t=="Q&A Bot")), inputs=task, outputs=input_question)
+    btn = gr.Button("Run")
+    btn.click(process, inputs=[task, input_text, input_question], outputs=output)
+
+demo.launch()
+