Update app.py

app.py

@@ -1,95 +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()
-
-
-gr.Interface.load("models/s3nh/pythia-410m-70k-steps-self-instruct-polish").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()