d

app.py

@@ -1,7 +1,42 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import gradio as gr
+import spaces
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load the pre-trained model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("merve/chatgpt-prompts-bart-long")
+model = AutoModelForSeq2SeqLM.from_pretrained("merve/chatgpt-prompts-bart-long", from_tf=True)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+@spaces.GPU
+def generate(prompt):
+    # Tokenize the input prompt
+    batch = tokenizer(prompt, return_tensors="pt")
+    
+    # Generate response using the model
+    generated_ids = model.generate(batch["input_ids"], max_new_tokens=150)
+    
+    # Decode the output into text
+    output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+    return output[0]
+
+# Gradio input component to accept character details
+input_component = gr.Textbox(label="Input a persona or character role, e.g., warrior, scientist", value="warrior")
+output_component = gr.Textbox(label="Generated Character Description")
+
+# Example character roles for the user to select from
+examples = [["warrior"], ["scientist"], ["mage"], ["detective"], ["robotic engineer"]]
+
+# Description for the Gradio interface
+description = (
+    "This app generates a character description based on a persona. "
+    "You can input a specific role or persona, such as 'warrior' or 'scientist', and the AI will generate a detailed description."
+)
+
+# Create the Gradio interface
+gr.Interface(
+    generate, 
+    inputs=input_component, 
+    outputs=output_component, 
+    examples=examples, 
+    title="🧙 Character Creator using AI 🧙", 
+    description=description
+).launch()