Spaces:
Sleeping
Sleeping
File size: 4,016 Bytes
e574b74 1831c48 c1ea7de e574b74 5c05c21 e574b74 1831c48 e574b74 af88ed0 1831c48 5c05c21 1831c48 19d8658 1831c48 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
import gradio as gr
import requests
import torch
import torch.nn as nn
import re
import os
from transformers import AutoTokenizer
MODEL = "cardiffnlp/xlm-twitter-politics-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
class ScorePredictor(nn.Module):
def __init__(self, vocab_size, embedding_dim=128, hidden_dim=256, output_dim=1):
super(ScorePredictor, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
self.fc = nn.Linear(hidden_dim, output_dim)
self.sigmoid = nn.Sigmoid()
def forward(self, input_ids, attention_mask):
embedded = self.embedding(input_ids)
lstm_out, _ = self.lstm(embedded)
final_hidden_state = lstm_out[:, -1, :]
output = self.fc(final_hidden_state)
return self.sigmoid(output)
score_model = ScorePredictor(tokenizer.vocab_size)
score_model.load_state_dict(torch.load("score_predictor.pth"))
score_model.eval()
def preprocess_text(text):
text = text.lower()
text = re.sub(r'http\S+', '', text)
text = re.sub(r'[^a-zA-Z0-9\s.,!?]', '', text)
text = re.sub(r'\s+', ' ', text).strip()
return text
def predict_sentiment(text):
if not text:
return 0.0
encoded_input = tokenizer(
text.split(),
return_tensors='pt',
padding=True,
truncation=True,
max_length=512
)
input_ids, attention_mask = encoded_input["input_ids"], encoded_input["attention_mask"]
with torch.no_grad():
score = score_model(input_ids, attention_mask)[0].item()
return score
def fetch_articles(ticker):
POLYGON_API_KEY = os.get_env('poly_api')
url = f"https://api.polygon.io/v2/reference/news?ticker={ticker}&limit=1&apiKey={POLYGON_API_KEY}"
try:
response = requests.get(url)
data = response.json()
if "results" in data and len(data["results"]) > 0:
article = data["results"][0]
title = article.get("title", "")
description = article.get("description", "")
return [title + " " + description]
else:
return [f"No news articles found for {ticker}."]
except Exception as e:
return [f"Error fetching articles for {ticker}: {str(e)}"]
def analyze_ticker(ticker):
articles = fetch_articles(ticker)
sentiments = []
for article in articles:
clean_text = preprocess_text(article)
sentiment = predict_sentiment(clean_text)
# Determine sentiment label
if sentiment > 0.6:
sentiment_label = "Negative"
emoji = "😊"
elif sentiment < 0.4:
sentiment_label = "Positive"
emoji = "😞"
else:
sentiment_label = "Neutral"
emoji = "😐"
sentiments.append({
"article": article,
"sentiment": sentiment,
"sentiment_label": sentiment_label,
"emoji": emoji
})
return sentiments
def gradio_interface(ticker):
results = analyze_ticker(ticker)
output = f"""
<h2>Sentiment Analysis for {ticker}</h2>
<div style='border: 1px solid #ccc; padding: 15px; border-radius: 5px; margin-bottom: 20px;'>
<h3>Article:</h3>
<p>{results[0]['article']}</p>
<h3>Sentiment:</h3>
<p>Score: {results[0]['sentiment']:.4f}</p>
<p>Label: {results[0]['sentiment_label']} {results[0]['emoji']}</p>
</div>
"""
return output
# Create Gradio interface
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.Textbox(label="Enter Stock Ticker", placeholder="AAPL, MSFT, GOOGL..."),
outputs=gr.HTML(label="Sentiment Analysis Results"),
title="Stock News Sentiment Analyzer",
description="Enter a stock ticker to analyze the sentiment of recent news articles about that company.",
examples=[["AAPL"], ["MSFT"], ["TSLA"]]
)
iface.launch()
|