Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,28 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# 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])),
-    ))
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+# Load model and tokenizer from Hugging Face
+MODEL_NAME = "imrgurmeet/fine-tuned-sentiment-model"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+st.title("Fine-Tuned Sentiment Analyzer")
+
+user_input = st.text_area("Enter text for sentiment analysis:")
+
+if st.button("Analyze"):
+    if user_input.strip() != "":
+        # Tokenize input
+        inputs = tokenizer(user_input, return_tensors="pt")
+        # Forward pass
+        outputs = model(**inputs)
+        probs = F.softmax(outputs.logits, dim=-1)
+        # Get predicted sentiment
+        pred_class = torch.argmax(probs).item()
+        sentiment = ["Negative", "Neutral", "Positive"][pred_class]
+        st.write(f"**Sentiment:** {sentiment}")
+        st.write(f"**Confidence:** {probs[0][pred_class]:.2f}")
+    else:
+        st.warning("Please enter some text.")