Update app.py

app.py

@@ -1,32 +1,27 @@
-import streamlit as st
 
-import transformers
+import streamlit as st
 
-import torch
+from PIL import Image
 
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 
+# Define the model names or identifiers
 
+model1_name = "Winnie-Kay/Sentiment-Analysis-Roberta-bases"
 
-# Define the paths of the pre-trained models
-
-model1_path = "saisi/finetuned-Sentiment-classfication-ROBERTA-Base-model"
-
-model2_path = "saisi/finetuned-Sentiment-classfication-DISTILBERT-model"
-
-
+model2_name = "Winnie-Kay/Finetuned_BertModel_SentimentAnalysis"
 
 
 # Initialize the tokenizer and models for sentiment analysis
 
-tokenizer1 = AutoTokenizer.from_pretrained(model1_path)
+tokenizer1 = AutoTokenizer.from_pretrained(model1_name)
 
-model1 = AutoModelForSequenceClassification.from_pretrained(model1_path)
+model1 = AutoModelForSequenceClassification.from_pretrained(model1_name)
 
-tokenizer2 = AutoTokenizer.from_pretrained(model2_path)
+tokenizer2 = AutoTokenizer.from_pretrained(model2_name)
 
-model2 = AutoModelForSequenceClassification.from_pretrained(model2_path)
+model2 = AutoModelForSequenceClassification.from_pretrained(model2_name)
 
 
 
@@ -34,67 +29,151 @@ model2 = AutoModelForSequenceClassification.from_pretrained(model2_path)
 # Define a function to preprocess the text data
 
 def preprocess(text):
+
     new_text = []
+
+    # Replace user mentions with '@user'
+
     for t in text.split(" "):
+
         t = '@user' if t.startswith('@') and len(t) > 1 else t
-         # Replace links with 'http'
+
+        # Replace links with 'http'
+
         t = 'http' if t.startswith('http') else t
-        new_text.append(t)
-         # Join the preprocessed text
 
-        return " ".join(new_text)
+        new_text.append(t)
 
+    # Join the preprocessed text
 
+    return " ".join(new_text)
 
 
 # Define a function to perform sentiment analysis on the input text using model 1
 
 def sentiment_analysis_model1(text):
+
+    # Preprocess the input text
+
     text = preprocess(text)
+
+
     # Tokenize the input text using the pre-trained tokenizer
+
     encoded_input = tokenizer1(text, return_tensors='pt')
+
+
     # Feed the tokenized input to the pre-trained model and obtain output
+
     output = model1(**encoded_input)
+
+
     # Obtain the prediction scores for the output
+
     scores_ = output[0][0].detach().numpy()
+
+
     # Apply softmax activation function to obtain probability distribution over the labels
+
     scores_ = torch.nn.functional.softmax(torch.from_numpy(scores_), dim=0).numpy()
+
+
     # Format the output dictionary with the predicted scores
+
     labels = ['Negative', 'Positive']
+
     scores = {l:float(s) for (l,s) in zip(labels, scores_) }
-     # Return the scores
-    return scores
 
 
+    # Return the scores
+
+    return scores
 
 
 # Define a function to perform sentiment analysis on the input text using model 2
 
 def sentiment_analysis_model2(text):
+
+    # Preprocess the input text
+
     text = preprocess(text)
+
+
     # Tokenize the input text using the pre-trained tokenizer
-    encoded_input = tokenizer1(text, return_tensors='pt')
+
+    encoded_input = tokenizer2(text, return_tensors='pt')
+
+
     # Feed the tokenized input to the pre-trained model and obtain output
-    output = model1(**encoded_input)
+
+    output = model2(**encoded_input)
+
+
     # Obtain the prediction scores for the output
+
     scores_ = output[0][0].detach().numpy()
+
+
     # Apply softmax activation function to obtain probability distribution over the labels
+
     scores_ = torch.nn.functional.softmax(torch.from_numpy(scores_), dim=0).numpy()
+
+
     # Format the output dictionary with the predicted scores
-    labels = ['Negative', 'Positive']
+
+    labels = ['Negative', 'Neutral', 'Positive']
+
     scores = {l:float(s) for (l,s) in zip(labels, scores_) }
-     # Return the scores
+
+
+    # Return the scores
+
     return scores
 
 
 # Define the Streamlit app
+
 def app():
+
     # Define the app title
+
     st.title("Sentiment Analysis")
+
     # Define the input field
+
     text_input = st.text_input("Enter text:")
+
+
     # Define the model selection dropdown
+
     model_selection = st.selectbox("Select a model:", ["Model 1", "Model 2"])
+
+
     # Perform sentiment analysis when the submit button is clicked
+
     if st.button("Submit"):
-        if text_input
+
+        if text_input:
+
+            if model_selection == "Model 1":
+
+                # Perform sentiment analysis using model 1
+
+                scores = sentiment_analysis_model1(text_input)
+
+                st.write(f"Model 1 predicted scores: {scores}")
+
+            else:
+
+                # Perform sentiment analysis using model 2
+
+                scores = sentiment_analysis_model2(text_input)
+
+                st.write(f"Model 2 predicted scores: {scores}")
+
+
+
+
+
+
+