Update app.py

app.py

@@ -1,75 +1,188 @@
-import os
-os.system('pip install torch')
-os.system('pip install transformers')
-os.system('pip install scipy')
-os.system('pip install gradio')
+import streamlit as st
 
+import transformers
 
-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"
+import torch
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+
+
+
+# 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"
+
+
+
+
+# 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)
 
-# 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
-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()
+
+    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