Update app.py

app.py

@@ -51,45 +51,19 @@ def preprocess(text):
 
 def sentiment_analysis_model1(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
+    # 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
 
 
 
@@ -97,74 +71,31 @@ def sentiment_analysis_model1(text):
 # 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
-
-
+    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 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"])
+       # 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"])