Update app.py

app.py

@@ -1,35 +1,37 @@
 import gradio as gr
-#from transformers import AutoTokenizer, AutoModelForCausalLM
-from transformers import pipeline
-
 model_name = "vijjuk/codegen-350M-mono-python-18k-alpaca"
-pipe = pipeline("python-fine-tuning", model=model_name)
+demo = gr.load(model_name, src="models")
+
+demo.launch()
 
 
 
 
 
+#import gradio as gr
+#from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
 #base_model = AutoModelForCausalLM.from_pretrained(model_name)
 #tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 #tokenizer.pad_token = tokenizer.eos_token
 #tokenizer.padding_side = "right"
 
-def query(instruction, input):
-    prompt = f"""### Instruction:
-        Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.
-        ### Task:
-        {instruction}
-        ### Input:
-        {input}
-        ### Response:
-        """
-    #input_ids = tokenizer(prompt, return_tensors="pt", truncation=True)
-    #output_base = base_model.generate(input_ids=input_ids, max_new_tokens=500, do_sample=True, top_p=0.9,temperature=0.5)
-    #response = "{tokenizer.batch_decode(output_base.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}"
-    #return response
-    return pipe(prompt)[0]["prompt"]
-
-inputs = ["text", "text"]
-outputs = "text"
-iface = gr.Interface(fn=query, inputs=inputs, outputs=outputs)
-iface.launch()
+# def query(instruction, input):
+#    prompt = f"""### Instruction:
+#        Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.
+#       ### Task:
+#        {instruction}
+#        ### Input:
+#        {input}
+#        ### Response:
+#        """
+#    input_ids = tokenizer(prompt, return_tensors="pt", truncation=True)
+#    output_base = base_model.generate(input_ids=input_ids, max_new_tokens=500, do_sample=True, top_p=0.9,temperature=0.5)
+#    response = "{tokenizer.batch_decode(output_base.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}"
+#    return response
+
+#inputs = ["text", "text"]
+#outputs = "text"
+#iface = gr.Interface(fn=query, inputs=inputs, outputs=outputs)
+#iface.launch()