Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForTokenClassification, AutoModelForSequenceClassification
+import gradio as gr
+
+# Initialize sentiment analysis model and tokenizer
+sentiment_tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
+sentiment_model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
+sentiment_model.eval()
+
+# Initialize stock identification model and tokenizer
+ner_tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
+ner_model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
+ner_model.eval()
+
+# def get_advice(sentiment_label, stocks_mentioned):
+#     # Add your own logic for providing advice based on sentiment and stocks mentioned
+#     if sentiment_label == "Positive":
+#         advice = "Positive sentiment. Consider taking advantage of positive market trends."
+#     elif sentiment_label == "Negative":
+#         if stocks_mentioned:
+#             advice = f"Negative sentiment. Consider re-evaluating your position on stocks: {', '.join(stocks_mentioned)}."
+#         else:
+#             advice = "Negative sentiment. Consider monitoring the market for potential impacts."
+#     else:
+#         advice = "Neutral sentiment. The market may not be strongly influenced. Monitor for changes."
+
+#     return advice
+
+def predict_sentiment_and_stock_info(headline):
+    # 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"
+
+    # Named Entity Recognition (NER)
+    ner_inputs = ner_tokenizer(headline, return_tensors="pt")
+    with torch.no_grad():
+        ner_outputs = ner_model(**ner_inputs)
+
+    # # Identify stocks mentioned in the headline
+    # ner_predictions = torch.nn.functional.softmax(ner_outputs.logits, dim=-1).argmax(2)
+    # tokens = ner_tokenizer.convert_ids_to_tokens(ner_inputs['input_ids'][0].tolist())  # Use ner_inputs here
+    # entities = ner_tokenizer.convert_ids_to_tokens(ner_predictions[0].tolist())
+    # stocks_mentioned = [tokens[i] for i, entity in enumerate(entities) if entity.startswith("B")]
+
+    # # Advice based on sentiment and identified stocks
+    # advice = get_advice(sentiment_label, stocks_mentioned)
+
+    # return sentiment_label, advice
+        return sentiment_label
+
+# Gradio Interface
+'''iface = gr.Interface(
+    fn=predict_sentiment_and_stock_info,
+    inputs="text",
+    outputs=["text", "text"],
+    live=True,
+    title="Financial News Sentiment and Stock Analysis",
+    description="Enter a financial news headline to analyze its sentiment, identify mentioned stocks, and get advice on how to proceed."
+)
+iface.launch()'''
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_sentiment_and_stock_info,
+    inputs=[gr.Textbox(lines=2, label="Financial Statement")],
+    outputs=[
+        gr.Textbox(label="Sentiment"),
+        # gr.Textbox(label="Advice")
+    ],
+    live=True,
+    title="Financial Content Sentiment Analysis",
+    description="Enter a financial statement to analyze its sentiment."
+)
+
+iface.launch()