Spaces:
Sleeping
Sleeping
File size: 2,207 Bytes
c69bfef 1c56924 64962af fea7782 1c56924 fea7782 c69bfef 1c56924 64962af 1c56924 64962af fea7782 1c56924 c69bfef fea7782 |
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 |
import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import torch.nn.functional as F
from huggingface_hub import login
import os
# Authenticate with Hugging Face using token from environment variable
try:
hf_token = os.environ.get("HUGGINGFACE_TOKEN")
if hf_token:
login(hf_token)
else:
print("Warning: HUGGINGFACE_TOKEN not found in environment variables")
except Exception as e:
print(f"Authentication error: {e}")
# Load MentalBERT model & tokenizer
try:
MODEL_NAME = "mental/mental-bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(
MODEL_NAME,
num_labels=2,
problem_type="single_label_classification"
)
except Exception as e:
print(f"Error loading model: {e}")
raise
LABELS = {
"neutral": {"index": 0, "description": "Emotionally balanced or calm"},
"emotional": {"index": 1, "description": "Showing emotional content"}
}
def analyze_text(text):
# Tokenize input
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
# Get model predictions
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = F.softmax(logits, dim=-1)[0]
# Get emotion scores
emotions = {
label: float(probs[info["index"]])
for label, info in LABELS.items()
}
return emotions
# Create Gradio interface
iface = gr.Interface(
fn=analyze_text,
inputs=gr.Textbox(label="Enter text to analyze", lines=3),
outputs=gr.Json(label="Emotion Analysis"),
title="MentalBERT Emotion Analysis",
description="Analyze the emotional content of text using MentalBERT (specialized for mental health content)",
examples=[
["I feel really anxious about my upcoming presentation"],
["I've been feeling quite depressed lately"],
["I'm managing my stress levels well today"],
["Just had a great therapy session!"]
],
allow_flagging="never"
)
# Launch the interface with CORS support
iface.launch(share=True, server_name="0.0.0.0")
|