Update app.py

app.py

@@ -1,27 +1,30 @@
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-model_name = "vikas83/my-bert-model"  # Your model
-
-# Load the model & tokenizer
+# Load a proper text-generation model
+model_name = "gpt2"  # Replace with your own trained model if available
 model = AutoModelForCausalLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-# Create a text generation pipeline
+# Create a text-generation pipeline
 nlp_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 # Function to generate response
 def generate_response(text):
-    result = nlp_pipeline(text, max_length=50, do_sample=True)
-    return result[0]['generated_text']
+    try:
+        result = nlp_pipeline(text, max_length=50, do_sample=True)
+        return result[0]['generated_text']
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # Gradio UI
 iface = gr.Interface(
     fn=generate_response,
     inputs="text",
     outputs="text",
-    title="My BERT Model",
-    description="Enter text and see the response from the model!"
+    title="AI Text Generator",
+    description="Enter text and get AI-generated responses!"
 )
 
-iface.launch(server_name="0.0.0.0", server_port=7860)
+# Launch Gradio on a public URL
+iface.launch(server_name="0.0.0.0", server_port=7860, share=True)