Update app.py

app.py

@@ -1,16 +1,21 @@
 import gradio as gr
-from transformers import AutoModel, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-model_name = "vikas83/my-bert-model"  # Your uploaded model
+model_name = "vikas83/my-bert-model"  # Your model
 
-model = AutoModel.from_pretrained(model_name)
+# Load the model & tokenizer
+model = AutoModelForCausalLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+# Create a text generation pipeline
+nlp_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Function to generate response
 def generate_response(text):
-    inputs = tokenizer(text, return_tensors="pt")
-    outputs = model(**inputs)
-    return "Model processed successfully!"  # Modify as needed
+    result = nlp_pipeline(text, max_length=50, do_sample=True)
+    return result[0]['generated_text']
 
+# Gradio UI
 iface = gr.Interface(
     fn=generate_response,
     inputs="text",
@@ -19,6 +24,4 @@ iface = gr.Interface(
     description="Enter text and see the response from the model!"
 )
 
-# Enable public access
-public_url = iface.launch(share=True)
-print(f"Access the app at {public_url}")
+iface.launch(server_name="0.0.0.0", server_port=7860)