Update app.py

app.py

@@ -15,60 +15,75 @@ 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'
-        t = 'http' if t.startswith('http') else t
-        new_text.append(t)
-    # Join the preprocessed text
-    return " ".join(new_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'
+        t = 'http' if t.startswith('http') else t
+        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
+    # 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
 
 # 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 = tokenizer2(text, return_tensors='pt')
-    # Feed the tokenized input to the pre-trained model and obtain output
-    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', 'Neutral', 'Positive']
-    scores = {l:float(s) for (l,s) in zip(labels, scores_) }
-    # Return the scores
-    return scores
+    # Preprocess the input text
+    text = preprocess(text)
+
+    # Tokenize the input text using the pre-trained tokenizer
+    encoded_input = tokenizer2(text, return_tensors='pt')
+    
+    # Feed the tokenized input to the pre-trained model and obtain output
+    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', 'Neutral', 'Positive']
+    scores = {l:float(s) for (l,s) in zip(labels, 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
+    # 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