Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from datasets import load_dataset
+import pandas as pd
+
+# Load model and tokenizer
+model_name = "google/flan-t5-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Load CSV data (replace with your dataset logic)
+def load_data():
+    dataset = load_dataset("csv", data_files={"train": "train.csv"})  # Adjust path
+    return dataset["train"]
+
+# Generate text (inference)
+def generate_text(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
+    outputs = model.generate(**inputs, max_new_tokens=100)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Fine-tune button (simplified example)
+def fine_tune():
+    dataset = load_data()
+    # Add your training logic here (see previous examples)
+    return "Fine-tuning complete! (Note: Models reset when Space stops.)"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# FLAN-T5 Demo")
+    with gr.Tab("Generate Text"):
+        prompt = gr.Textbox(label="Input Prompt")
+        generate_btn = gr.Button("Generate")
+        output = gr.Textbox(label="Output")
+        generate_btn.click(fn=generate_text, inputs=prompt, outputs=output)
+    
+    with gr.Tab("Fine-Tune"):
+        train_btn = gr.Button("Train on CSV Data")
+        train_output = gr.Textbox(label="Training Status")
+        train_btn.click(fn=fine_tune, outputs=train_output)
+
+demo.launch()