Hamza3107 commited on
Commit
2f9bd26
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1 Parent(s): 8c28dd0

Update app.py

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  1. app.py +26 -2
app.py CHANGED
@@ -1,6 +1,30 @@
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  print("first test for hugging face")
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- # Load model directly
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
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  tokenizer = AutoTokenizer.from_pretrained("Remicm/sentiment-analysis-model-for-socialmedia")
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- model = AutoModelForSequenceClassification.from_pretrained("Remicm/sentiment-analysis-model-for-socialmedia")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  print("first test for hugging face")
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+ import gradio as gr
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ import torch
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+ # Load the tokenizer and model
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  tokenizer = AutoTokenizer.from_pretrained("Remicm/sentiment-analysis-model-for-socialmedia")
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+ model = AutoModelForSequenceClassification.from_pretrained("Remicm/sentiment-analysis-model-for-socialmedia")
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+
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+ # Function to predict sentiment
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+ def predict_sentiment(text):
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+ inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
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+ with torch.no_grad():
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+ outputs = model(**inputs)
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+ logits = outputs.logits
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+ predicted_class = torch.argmax(logits, dim=1).item()
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+
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+ # Define sentiment labels (adjust based on your model's output)
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+ sentiments = ["Negative", "Neutral", "Positive"]
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+ return sentiments[predicted_class]
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+
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+ # Create the Gradio interface
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+ interface = gr.Interface(fn=predict_sentiment,
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+ inputs="text",
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+ outputs="label",
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+ title="Sentiment Analysis of Instagram Comments",
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+ description="Enter a comment to determine its sentiment (Positive, Neutral, Negative).")
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+
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+ # Launch the interface
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+ interface.launch()