Update app.py

app.py

@@ -2,96 +2,46 @@ import streamlit as st
 import transformers
 from transformers import pipeline
 import re
-import base64
-
-# Set page config
-st.set_page_config(page_title="Telugu Sentiment Analysis", layout="centered")
-
-# Set background image
-def set_background(image_file):
-    with open(image_file, "rb") as file:
-        encoded = base64.b64encode(file.read()).decode()
-    st.markdown(
-        f"""
-        <style>
-        .stApp {{
-            background-image: url("data:image/jpg;base64,{encoded}");
-            background-size: cover;
-            background-position: center;
-            background-repeat: no-repeat;
-        }}
-        textarea {{
-            background-color: #f8f8f8 !important;
-            font-size: 18px !important;
-        }}
-        .custom-button {{
-            display: inline-block;
-            padding: 10px 25px;
-            font-size: 18px;
-            font-weight: bold;
-            color: white;
-            background-color: #ff4b4b;
-            border-radius: 10px;
-            text-align: center;
-        }}
-        </style>
-        """,
-        unsafe_allow_html=True
-    )
-
-set_background("New3.jpg")
 
 # Title and Description
-st.markdown("<h1 style='text-align: center;'>📊 Telugu Sentiment Analysis</h1>", unsafe_allow_html=True)
-st.markdown(
-    "<div style='text-align:center;'>"
-    "Analyze the sentiment (Positive, Negative, Neutral) of a given <strong>Telugu</strong> sentence using a fine-tuned BERT model."
-    "</div><br>", unsafe_allow_html=True
-)
+st.set_page_config(page_title="Telugu Sentiment Analysis", layout="centered")
+st.title("📊 Telugu Sentiment Analysis")
+st.markdown("Analyze the sentiment (Positive, Negative, Neutral) of a given **Telugu** sentence using a fine-tuned BERT model.")
 
-# Load pipeline
+# Load the model pipeline
 @st.cache_resource
 def load_pipeline():
     return pipeline("text-classification", model="Adityaganesh/Telugu_Sentiment_Analysis")
 
 pipe = load_pipeline()
 
-# Preprocess text
+# Optional: Text Preprocessing (basic cleaning)
 def preprocess_text(text):
     text = text.strip()
     text = re.sub(r"\s+", " ", text)
     return text
 
-# Telugu validation
-def is_telugu(text):
-    return bool(re.fullmatch(r"[\u0C00-\u0C7F\s]+", text))
-
-# Input
-user_input = st.text_area("✍️ Enter Telugu Text:", height=200, placeholder="ఇక్కడ మీ తెలుగు వాక్యాన్ని నమోదు చేయండి...")
+# User Input
+user_input = st.text_area("Enter Telugu Text:", height=200, placeholder="ఇక్కడ మీ తెలుగు వాక్యాన్ని నమోదు చేయండి...")
 
-# Button
-if st.button("🔍 Predict"):
+if st.button("🔍 Analyze Sentiment"):
     if user_input.strip() == "":
         st.warning("దయచేసి కొన్ని తెలుగు వాక్యాలు నమోదు చేయండి.")
-    elif not is_telugu(user_input):
-        st.error("దయచేసి కేవలం తెలుగు వాక్యాలు మాత్రమే నమోదు చేయండి.")
     else:
         clean_text = preprocess_text(user_input)
-        with st.spinner("విశ్లేషణ జరుగుతోంది..."):
+        with st.spinner("Analyzing sentiment..."):
             result = pipe(clean_text)[0]
             idx = int(result['label'].split('_')[1])
             
             if idx == 0:
                 sentiment = "😐 Neutral"
-                color = "#808080"
+                color = "gray"
             elif idx == 1:
                 sentiment = "😊 Positive"
-                color = "#28a745"
+                color = "green"
             else:
                 sentiment = "😠 Negative"
-                color = "#dc3545"
+                color = "red"
 
-            st.markdown(
-                f"<h3 style='color:{color}; text-align:center;'>📢 Prediction: {sentiment}</h3>",
-                unsafe_allow_html=True
-            )
+            st.markdown(f"### Prediction: <span style='color:{color}'>{sentiment}</span>", unsafe_allow_html=True)
+            st.markdown(f"**Confidence:** {result['score']:.2f}")