Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+MODEL_NAME = "yiyanghkust/finbert-tone"
+
+# Load FinBERT
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+def analyze_financial_text(text: str):
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=512,
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    probs = F.softmax(outputs.logits, dim=-1)[0]
+
+    id2label = model.config.id2label
+    result = {id2label[i]: float(prob) for i, prob in enumerate(probs)}
+
+    return result
+
+demo = gr.Interface(
+    fn=analyze_financial_text,
+    inputs=gr.Textbox(
+        label="Enter financial news / earnings call text:",
+        lines=6,
+        placeholder="Paste financial text here..."
+    ),
+    outputs=gr.Label(
+        label="Sentiment (Positive / Neutral / Negative)",
+        num_top_classes=3
+    ),
+    title="FinBERT Financial Sentiment Analyzer",
+    description="Analyze the sentiment of financial news or corporate disclosures using the FinBERT model."
+)
+
+if __name__ == "__main__":
+    demo.launch()