Update app.py

app.py

@@ -12,13 +12,28 @@ output = query({
 	"inputs": "I like you. I love you",
 })
 
+
+# def predict_sentiment(payload):
+#     # Sentiment Analysis
+#     sentiment_inputs = sentiment_tokenizer(headline, padding=True, truncation=True, return_tensors='pt')
+#     with torch.no_grad():
+#         sentiment_outputs = sentiment_model(**sentiment_inputs)
+#         sentiment_prediction = torch.nn.functional.softmax(sentiment_outputs.logits, dim=-1)
+
+#     pos, neg, neutr = sentiment_prediction[:, 0].item(), sentiment_prediction[:, 1].item(), sentiment_prediction[:, 2].item()
+#     sentiment_label = "Positive" if pos > neg and pos > neutr else "Negative" if neg > pos and neg > neutr else "Neutral"
+
+
+#     return sentiment_label
+
+
+
 # Gradio Interface
 iface = gr.Interface(
-    fn=predict_sentiment_and_stock_info,
+    fn=query,
     inputs=[gr.Textbox(lines=2, label="Financial Statement")],
     outputs=[
         gr.Textbox(label="Sentiment"),
-        gr.Textbox(label="Advice")
     ],
     live=True,
     title="Financial Content Sentiment Analysis",