Upload app.py

app.py

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+import gradio as gr
+import json
+
+from datetime import datetime
+import demoji
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+import re
+from transformers import pipeline
+from uuid import uuid4
+
+#based on https://huggingface.co/spaces/Wauplin/space_to_dataset_saver/blob/main/app_json.py
+#data is saved at https://huggingface.co/datasets/MR17u/tweeteval-irony-mcc/tree/main
+
+# JSON_DATASET_DIR = Path("json_dataset")
+# JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)
+
+# JSON_DATASET_PATH = JSON_DATASET_DIR / f"data-{uuid4()}.json"
+
+
+prompt = '''### Instruction:
+Classify if the following tweet is ironic or not
+### Input:
+{text}
+### Response:
+'''
+
+
+# scheduler = CommitScheduler(
+#     repo_id="tweeteval-irony-mcc",
+#     repo_type="dataset",
+#     folder_path=JSON_DATASET_DIR,
+#     path_in_repo="data",
+# )
+
+classifier = pipeline("text-generation", model="meta-llama/Llama-2-7b-hf")
+classifier.load_lora_weights("PierreEpron/llama7b-irony", weight_name="adapter_model.safetensors")
+
+def clean_brackets(text):
+    return text.replace('{', '(').replace('}', ')')
+
+def clean_emojis(text, type:str = ''):
+    if type=='rem':
+        return demoji.replace(text, '')
+    elif type!='keep':
+        return demoji.replace_with_desc(text, type)
+    else:
+        return text
+
+def clean_hashtags(text, hashtags=['#irony', '#sarcasm','#not']):
+    for hashtag in hashtags:
+        text = re.sub(hashtag, '', text, flags=re.I)
+    return re.sub(r' +', r' ', text)
+
+def clean_text(text):
+    return re.sub(' {2,}', ' ',clean_emojis(clean_hashtags(clean_brackets(text)))).strip()
+
+# def save_json(entry: str, result) -> None:
+#     with scheduler.lock:
+#         with JSON_DATASET_PATH.open("a") as f:
+#             result = json.loads(result.replace("'",'"'))[0]
+#             json.dump({"entry": entry, "label": result['label'], "score": result['score'], "datetime": datetime.now().isoformat()}, f)
+#             f.write("\n")
+
+def classif(text: str):
+    return classifier(prompt.format(text=clean_text(text)))
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        entry = gr.Textbox(label="Input")
+        result = gr.Textbox(label="Classification")
+    input_btn = gr.Button("Submit")
+    input_btn.click(fn=classif, inputs=entry, outputs=result).success(
+        fn=print, #save_json,
+        inputs=[entry, result],
+        outputs=None
+        )
+
+demo.launch()