Update app.py

app.py

@@ -1,188 +1,69 @@
-import streamlit as st
-
-import transformers
-
-import torch
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-
-
-
-# Define the paths of the pre-trained models
-
-model1_path = "Winnie-Kay/Finetuned_BertModel_SentimentAnalysis"
-
-model2_path = "Winnie-Kay/Sentiment-Analysis-Roberta-bases"
-
-
-
-
-# Initialize the tokenizer and models for sentiment analysis
-
-tokenizer1 = AutoTokenizer.from_pretrained(model1_path)
-
-model1 = AutoModelForSequenceClassification.from_pretrained(model1_path)
-
-tokenizer2 = AutoTokenizer.from_pretrained(model2_path)
-
-model2 = AutoModelForSequenceClassification.from_pretrained(model2_path)
-
-
-
+import numpy as np
+from scipy.special import softmax
+import gradio as gr
+from transformers import (
+    AutoTokenizer,
+    AutoConfig, 
+    AutoModelForSequenceClassification,
+    TFAutoModelForSequenceClassification)
+# Define the model path where the pre-trained model is saved on the Hugging Face model hub
+model_path = "Winnie-Kay/Finetuned_bert_model"
+
+# Initialize the tokenizer for the pre-trained model
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+# Load the configuration for the pre-trained model
+config = AutoConfig.from_pretrained(model_path)
+
+# Load the pre-trained model
+model = AutoModelForSequenceClassification.from_pretrained(model_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)
-
-
-
-
-# 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
-
-
-
-
-# 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
-
-
-
-
-# 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
+    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
+def sentiment_analysis(text):
+    # Preprocess the input text
+    text = preprocess(text)
+
+    # Tokenize the input text using the pre-trained tokenizer
+    encoded_input = tokenizer(text, return_tensors='pt')
+    
+    # Feed the tokenized input to the pre-trained model and obtain output
+    output = model(**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_ = softmax(scores_)
+    
+    # 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 a Gradio interface to interact with the model
+demo = gr.Interface(
+    fn=sentiment_analysis, # Function to perform sentiment analysis
+    inputs=gr.Textbox(placeholder="Write your tweet here..."), # Text input field
+    outputs="label", # Output type (here, we only display the label with the highest score)
+    interpretation="default", # Interpretation mode
+    examples=[["Have Fun with it...will be updated soon!"]],# Example input(s) to display on the interface
+    image=gr.Image("https://www.reputationx.com/hubfs/what-is-sentiment-analysis-cover.jpg"),
+    css= "body {background-color: black}"
+)
+
+# Launch the Gradio interface
+demo.launch()