Create app.py

app.py

@@ -0,0 +1,37 @@
+import torch
+import gradio as gr
+from transformers import pipeline, logging, AutoModelForCausalLM, AutoTokenizer
+
+model_name = "microsoft/phi-2"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True
+)
+model.config.use_cache = False
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+# peft_model_folder = 'ckpts'
+# model.load_adapter(peft_model_folder)
+
+def generate_text(input_text):
+  pipe = pipeline(task="text-generation",model=model,tokenizer=tokenizer, max_length=200)
+  result = pipe(f"{input_text}")
+  return_answer = (result[0]['generated_text']).replace(input_text,'')
+  return return_answer
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_text,  # Function to be called on user input
+    inputs=gr.Textbox(
+            label="Ask question?", 
+            info="Enter your prompt:"
+        ),
+    outputs=gr.Textbox(
+            label="Response from AI Model: ", 
+        ),
+)
+
+# Launch the Gradio app
+iface.launch()