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import gradio as gr
from zipfile import ZipFile
import os
# Importing the sentiment_analysis function
def sentiment_analysis(dated_input):
tokenizer = AutoTokenizer.from_pretrained("aliciiavs/sentiment-analysis-whatsapp2")
model = AutoModelForSequenceClassification.from_pretrained("aliciiavs/sentiment-analysis-whatsapp2")
# Tokenize input
inputs = tokenizer(dated_input, padding=True, return_tensors="pt")
# Forward pass through the model
with torch.no_grad():
outputs = model(**inputs)
# Get predicted probabilities and predicted label
probabilities = torch.softmax(outputs.logits, dim=1)
predicted_label = torch.argmax(probabilities, dim=1)
# Convert the predicted label tensor to a Python integer
predicted_label = predicted_label.item()
# Map predicted label index to sentiment label
label_dic = {0: 'sadness', 1: 'joy', 2: 'love', 3: 'anger', 4: 'fear', 5: 'surprise'}
# Return the predicted sentiment label instead of printing it
return label_dic[predicted_label]
# Define a Gradio interface
def sentiment_analysis_interface(zip_file):
# Extract text from zip file
with ZipFile(zip_file) as archive:
# Assuming each file in the zip contains text
text = ""
for filename in archive.namelist():
with archive.open(filename) as file:
text += file.read().decode("utf-8")
# Perform sentiment analysis
predicted_sentiment = sentiment_analysis(text)
# Return the predicted sentiment label
return f"Predicted sentiment label: {predicted_sentiment}"
# Create a Gradio interface
gr.Interface(
fn=sentiment_analysis_interface,
inputs=gr.File(type="filepath", label="Upload a zip file"),
outputs="text"
).launch()
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