Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,46 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+print("selam")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.title("Sentiment Analysis with BERT")
+
+user_input = st.text_area("Enter your text here:")
+
+
+
+if st.button("Analyze"):
+
+    tokenizer = BertTokenizer.from_pretrained("ulascelenk/egitim")
+    model = BertForSequenceClassification.from_pretrained("ulascelenk/egitim")
+
+    inputs = tokenizer.encode_plus(
+    user_input,
+    return_tensors='pt',       
+    truncation=True,            
+    max_length=128,             
+    padding='max_length'        
+    )
+
+    with torch.no_grad():
+        input_ids = inputs['input_ids']
+        attention_mask = inputs['attention_mask']
+        outputs = model(input_ids, attention_mask=attention_mask)
+        logits = outputs.logits
+
+    
+    predicted_class = torch.argmax(logits, dim=1).item()
+
+    sentiment_dict = {
+    0: "Mild Negative",
+    1: "Mild Positive",
+    2: "Neutral",
+    3: "Strong Negative",
+    4: "Strong Positive"
+    }
+
+    
+    st.write(f"Predicted class: {sentiment_dict[predicted_class]}")
+
+
+