Update app.py

app.py

@@ -1,92 +1,95 @@
-import pathlib
+# import pathlib
 import gradio as gr
-import transformers
-from transformers import AutoTokenizer
-from transformers import ModelForCausalLM 
-from transformers import GenerationConfig
-from typing import List, Dict, Union
-from typing import Any, TypeVar
-
-Pathable = Union[str, pathlib.Path]
-
-def load_model(name: str) -> Any:
-    return ModelForCausalLM.from_pretrained(name)
-
-def load_tokenizer(name: str) -> Any:
-    return AutoTokenizer.from_pretrained(name)
-
-def create_generator():
-    return GenerationConfig(
-    temperature=1.0,
-    top_p=0.75,
-    num_beams=4,
-)
+# import transformers
+# from transformers import AutoTokenizer
+# from transformers import ModelForCausalLM 
+# from transformers import GenerationConfig
+# from typing import List, Dict, Union
+# from typing import Any, TypeVar
+
+# Pathable = Union[str, pathlib.Path]
+
+# def load_model(name: str) -> Any:
+#     return ModelForCausalLM.from_pretrained(name)
+
+# def load_tokenizer(name: str) -> Any:
+#     return AutoTokenizer.from_pretrained(name)
+
+# def create_generator():
+#     return GenerationConfig(
+#     temperature=1.0,
+#     top_p=0.75,
+#     num_beams=4,
+# )
     
-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:"""
-
-
-
-
-def evaluate(instruction, input=None):
-    prompt = generate_prompt(instruction, input)
-    inputs = tokenizer(prompt, return_tensors="pt")
-    input_ids = inputs["input_ids"].cuda()
-    generation_output = model.generate(
-        input_ids=input_ids,
-        generation_config=generation_config,
-        return_dict_in_generate=True,
-        output_scores=True,
-        max_new_tokens=256
-    )
-    for s in generation_output.sequences:
-        output = tokenizer.decode(s)
-        print("Response:", output.split("### Response:")[1].strip())
-
-
-def inference(text):
-    output = evaluate(instruction = instruction, input = input)
-    return output
-
-io = gr.Interface(
-    inference, 
-    gr.Textbox(
-        lines = 3, max_lines = 10, 
-        placeholder = "Add question here", 
-        interactive = True, 
-        show_label = False
-    ), 
-    gr.Textbox(
-        lines = 3, 
-        max_lines = 25, 
-        placeholder = "add context here", 
-        interactive = True, 
-        show_label  = False
-    ), 
-    outputs =[
-    gr.Textbox(lines = 2, label = 'Pythia410m output', interactive = False)
-    ]
-    ), 
-    title = title, 
-    description = description, 
-    article = article, 
-    examples = examples, 
-    cache_examples = False, 
-)
-io.launch()
+# 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:"""
+
+
+
+
+# def evaluate(instruction, input=None):
+#     prompt = generate_prompt(instruction, input)
+#     inputs = tokenizer(prompt, return_tensors="pt")
+#     input_ids = inputs["input_ids"].cuda()
+#     generation_output = model.generate(
+#         input_ids=input_ids,
+#         generation_config=generation_config,
+#         return_dict_in_generate=True,
+#         output_scores=True,
+#         max_new_tokens=256
+#     )
+#     for s in generation_output.sequences:
+#         output = tokenizer.decode(s)
+#         print("Response:", output.split("### Response:")[1].strip())
+
+
+# def inference(text):
+#     output = evaluate(instruction = instruction, input = input)
+#     return output
+
+# io = gr.Interface(
+#     inference, 
+#     gr.Textbox(
+#         lines = 3, max_lines = 10, 
+#         placeholder = "Add question here", 
+#         interactive = True, 
+#         show_label = False
+#     ), 
+#     gr.Textbox(
+#         lines = 3, 
+#         max_lines = 25, 
+#         placeholder = "add context here", 
+#         interactive = True, 
+#         show_label  = False
+#     ), 
+#     outputs =[
+#     gr.Textbox(lines = 2, label = 'Pythia410m output', interactive = False)
+#     ]
+#     ), 
+#     title = title, 
+#     description = description, 
+#     article = article, 
+#     examples = examples, 
+#     cache_examples = False, 
+# )
+# io.launch()
+
+
+gr.Interface.load("models/s3nh/pythia-410m-70k-steps-self-instruct-polish").launch()