initial space deployment

app.py

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+import gradio as gr
+from transformers import AutoTokenizer
+import torch
+from tiny_finbert import TinyFinBERTRegressor, preprocess_texts
+import os
+
+MODEL_DIR = "./saved_model"
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
+
+model = TinyFinBERTRegressor().to(DEVICE)
+model.load_state_dict(torch.load(os.path.join(MODEL_DIR, "regressor_model.pt"), map_location=DEVICE))
+model.eval()
+
+def predict_sentiment(text):
+    processed = preprocess_texts([text])[0]
+    inputs = tokenizer(processed, return_tensors="pt", truncation=True, padding='max_length', max_length=128)
+    inputs = {k: v.to(DEVICE) for k, v in inputs.items() if k != "token_type_ids"}
+    with torch.no_grad():
+        score = model(**inputs)["score"].item()
+
+    if score > 0.3:
+        interpretation = "positive"
+    elif score < -0.3:
+        interpretation = "negative"
+    else:
+        interpretation = "neutral"
+    return {"score": round(score, 4), "interpretation": interpretation}
+
+iface = gr.Interface(fn=predict_sentiment,
+                     inputs=gr.Textbox(label="Enter financial sentence"),
+                     outputs=[
+                         gr.Number(label="Sentiment Score"),
+                         gr.Textbox(label="Interpretation")
+                     ],
+                     title="TinyFinBERT Sentiment Analysis")
+
+iface.launch()

requirements.txt

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+torch>=1.10.0
+accelerate>=0.20.1
+transformers
+gradio
+pydantic
+pandas
+scikit-learn
+spacy
+nltk
+datasets
+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1.tar.gz

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