Update app.py

app.py

@@ -55,30 +55,14 @@ else:
         device_map={"": device},
     )
 
-def generate_prompt(instruction, input=None):
-    if input:
-        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-### Instruction:
-{instruction}
-### Input:
-{input}
-### Response:"""
-    else:
-        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-### Instruction :
-{instruction}
-### Response :"""
-
 if device != "cpu":
     model.half()
 model.eval()
 if torch.__version__ >= "2":
     model = torch.compile(model)
 
-
 def evaluate(
-    instruction,
-    input=None,
+    input_text,
     temperature=0.1,
     top_p=0.75,
     top_k=40,
@@ -86,7 +70,7 @@ def evaluate(
     max_new_tokens=128,
     **kwargs,
 ):
-    prompt = generate_prompt(instruction, input)
+    prompt = input_text
     inputs = tokenizer(prompt, return_tensors="pt")
     input_ids = inputs["input_ids"].to(device)
     generation_config = GenerationConfig(
@@ -106,27 +90,16 @@ def evaluate(
         )
     s = generation_output.sequences[0]
     output = tokenizer.decode(s)
-    return output.split("### Response:")[1].strip()
+    return output.strip()
 
 g = gr.Interface(
     fn=evaluate,
-    inputs=[
-        gr.inputs.Textbox(
-            label="Instruction",
-            placeholder="Type your instruction here...",
-            lines=3
-        ),
-        gr.inputs.Textbox(
-            label="Input",
-            placeholder="Type additional input here...",
-            lines=3
-        ),
-    ],
+    inputs=gr.inputs.Textbox(label="Input", placeholder="Type your input here..."),
     outputs=gr.outputs.Textbox(label="Output"),
-    title="Instruction-based Text Generation",
-    description="Enter an instruction and optional input, and the model will generate a response based on the instruction.",
+    title="Text Generation",
+    description="Enter an input, and the model will generate a response based on the input.",
     theme="default",
 )
 
 if __name__ == "__main__":
-    g.launch()   
+    g.launch()