Add application file

app.py

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+import gradio as gr
+
+from GPTagger import *
+from langchain.prompts import PromptTemplate
+
+default_prompt = """
+Please understand the instructions above and do extraction in the text below.
+
+TEXT:
+\"\"\"
+{text}
+\"\"\"
+"""
+
+def ner(
+    model: str,
+    nr_call: int,
+    tag_name: str,
+    tag_max_len: int,
+    text: str,
+    prompt: str,
+):
+    cfg = NerConfig(
+        tag_name=tag_name,
+        model=model,
+        nr_calls=nr_call,
+        tag_max_len=tag_max_len,
+    )
+
+    ner_pipeline = NerPipeline.from_config(cfg)
+    template = PromptTemplate.from_template(prompt)
+
+    extractions = ner_pipeline(text, template, "")
+
+    if not extractions:
+        output = []
+    else:
+        output = [
+            {"entity": tag_name.upper(), "start": item.start, "end": item.end}
+            for item in extractions
+        ]
+    return {"text": text, "entities": output}
+
+
+with gr.Blocks(theme=gr.themes.Default(text_size=gr.themes.sizes.text_lg)) as demo:
+    with gr.Row():
+        tag_name = gr.Textbox(label="tag name")
+        tag_max_len = gr.Slider(
+            minimum=10, maximum=1000, step=10, label="max length of the tag"
+        )
+    with gr.Row():
+        model = gr.Dropdown(
+            ["gpt-3.5-turbo-0613", "gpt-4-0613"],
+            label="model_name",
+            value="gpt-3.5-turbo-0613",
+        )
+        nr_call = gr.Number(label="nr_of_calls", minimum=1, value=1, precision=0)
+    with gr.Row():
+        prompt = gr.TextArea(
+            placeholder="Enter your prompt here...",
+            label="prompt",
+            value=default_prompt,
+        )
+        text = gr.TextArea(placeholder="Enter your text here...", label="text")
+    btn = gr.Button("Submit")
+    output = gr.HighlightedText()
+    btn.click(
+        ner,
+        inputs=[
+            model,
+            nr_call,
+            tag_name,
+            tag_max_len,
+            text,
+            prompt,
+        ],
+        outputs=output,
+    )
+
+demo.launch()

requirements.txt

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+gptagger