Update app.py

app.py

@@ -1,18 +1,15 @@
+# Load model directly
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
 
-# Load the model and tokenizer
+
 model = AutoModelForCausalLM.from_pretrained("MohamedTalaat91/gpt2-wikitext2")
 tokenizer = AutoTokenizer.from_pretrained("MohamedTalaat91/gpt2-tokenizer")
 
-# Updated generate function
-def generate(messages, state):
-    # The last message content is a dictionary with "role" and "content"
-    input_text = messages[-1]  # Extracting the content of the last message
-    
-    # Tokenize the input
+
+
+def generate(input_text) :
     inputs = tokenizer(input_text, return_tensors="pt")
-    
     # Generate text based on the input
     generated_ids = model.generate(
         inputs['input_ids'], 
@@ -22,16 +19,21 @@ def generate(messages, state):
         top_k=50,  # Top-k sampling to introduce diversity
         temperature=0.7  # Controls randomness in sampling
     )
-    
-    # Decode the generated text
     generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-    
-    # Prepare the response in the correct format
-    bot_message = {"role": "bot", "content": generated_text}
-    messages.append(bot_message)  # Add the bot's message to the conversation
-    
-    return messages, state  # Return the updated messages and state
 
+    return generated_text
+
+
+import gradio as gr
+
+with gr.Blocks() as demo:
+    gr.Markdown("# GPT-2 WikiText2")
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Input Text")
+            generate_button = gr.Button("Generate")
+            output_text = gr.Textbox(label="Generated Text")
+
+    generate_button.click(fn=generate, inputs=input_text, outputs=output_text)
 
-# Launch Gradio interface with the updated function
-gr.ChatInterface(generate, type="messages").launch(share=True)
+demo.launch(share=True)