Update app.py

app.py

@@ -1,5 +1,5 @@
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-from transformers import pipeline
+# from transformers import pipeline
 import torch
 import gradio as gr
 from huggingface_hub import InferenceClient
@@ -12,9 +12,9 @@ model = AutoModelForSeq2SeqLM.from_pretrained("Kaludi/chatgpt-gpt4-prompts-bart-
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 # pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha",torch_dtype=torch.bfloat16, device_map="auto")
 
-def generateZep(inputuno, inputdos):
+def generateZep(inputuno):
     prompt = inputuno
-    promptdos = inputdos
+    # promptdos = inputdos
     
     generate_kwargs = dict(
         temperature=0.9,
@@ -39,9 +39,9 @@ def generateZep(inputuno, inputdos):
 #        },
 #    ]
 
-    formatted_prompt = f"<s>[INST] {new_prompt} {promptdos} [/INST]"
+    formatted_prompt = f"<s>[INST] {new_prompt} [/INST]"
     
-    stream = client.text_generation(formatted_prompt, stream=True, details=True, return_full_text=False)
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
     output = ""
 
     for response in stream:
@@ -60,6 +60,6 @@ output_component = gr.Textbox(label="Output: ")
 examples = [["photographer"], ["developer"], ["teacher"], ["human resources staff"], ["recipe for ham croquettes"]]
 description = ""
 
-PerfectGPT = gr.Interface(fn=generateZep, inputs=[input_prompt, input_promptdos], outputs=output_component, examples=examples, title="🗿 PerfectGPT v1 🗿", description=description)
+PerfectGPT = gr.Interface(fn=generateZep, inputs=input_prompt, outputs=output_component, examples=examples, title="🗿 PerfectGPT v1 🗿", description=description)
 
 PerfectGPT.launch()