Update app.py

app.py

@@ -1,79 +1,27 @@
-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
-
-# 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()
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/ProsusAI/finbert"
+headers = {"Authorization": "Bearer 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"}
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+	
+output = query({
+	"inputs": "I like you. I love you",
+})
+
+# # 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()