Create app.py

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "google/flan-t5-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+def concatenate_and_generate(text1, text2, temperature, top_p):
+    concatenated_text = text1 + " " + text2
+    inputs = tokenizer(concatenated_text, return_tensors="pt")
+    
+    # Generate the output with specified temperature and top_p
+    output = model.generate(
+        inputs["input_ids"], 
+        do_sample=True, 
+        temperature=temperature, 
+        top_p=top_p,
+        max_length=100
+    )
+    
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+# Define Gradio interface
+inputs = [
+    gr.inputs.Textbox(lines=2, placeholder="Enter first text here..."),
+    gr.inputs.Textbox(lines=2, placeholder="Enter second text here..."),
+    gr.inputs.Slider(0.1, 1.0, 0.7, step=0.1, label="Temperature"),
+    gr.inputs.Slider(0.1, 1.0, 0.9, step=0.1, label="Top-p")
+]
+outputs = gr.outputs.Textbox()
+
+gr.Interface(
+    fn=concatenate_and_generate, 
+    inputs=inputs, 
+    outputs=outputs, 
+    title="Text Concatenation and Generation with FLAN-T5",
+    description="Concatenate two input texts and generate an output using google/flan-t5-large. Adjust the temperature and top_p parameters for different generation behaviors."
+).launch()