Update app.py

app.py

@@ -1,3 +1,78 @@
+# import streamlit as st
+# from transformers import pipeline
+# import re
+# import base64
+
+# # Page Configuration
+# st.set_page_config(page_title="Telugu Sentiment Analysis", layout="centered", )
+
+# # Function to Encode Image
+# def get_base64(file_path):
+#     with open(file_path, "rb") as f:
+#         data = f.read()
+#     return base64.b64encode(data).decode()
+
+# # Set Background from Image
+# def set_background(image_path):
+#     img_data = get_base64(image_path)
+#     st.markdown(
+#         f"""
+#         <style>
+#         .stApp {{
+#             background-image: url("data:image/jpg;base64,{img_data}");
+#             background-size: cover;
+#             background-position: center;
+#             background-repeat: no-repeat;
+#         }}
+#         </style>
+#         """,
+#         unsafe_allow_html=True
+#     )
+
+# # Set the background
+# set_background("New3.jpg")
+
+# # Title and Description
+# st.title("📊 Telugu Sentiment Analysis")
+# st.markdown("Analyze the sentiment (Positive, Negative, Neutral) of a given **Telugu** sentence using a fine-tuned BERT model.", #1affff)
+
+# # Load the model pipeline
+# @st.cache_resource
+# def load_pipeline():
+#     return pipeline("text-classification", model="Adityaganesh/Telugu_Sentiment_Analysis")
+
+# pipe = load_pipeline()
+
+# # Text Preprocessing
+# def preprocess_text(text):
+#     text = text.strip()
+#     text = re.sub(r"\s+", " ", text)
+#     return text
+
+# # User Input
+# user_input = st.text_area("Enter Telugu Text:", height=200, placeholder="ఇక్కడ మీ తెలుగు వాక్యాన్ని నమోదు చేయండి...")
+
+# if st.button("🔍 Analyze Sentiment"):
+#     if user_input.strip() == "":
+#         st.warning("దయచేసి కొన్ని తెలుగు వాక్యాలు నమోదు చేయండి.")
+#     else:
+#         clean_text = preprocess_text(user_input)
+#         with st.spinner("Analyzing sentiment..."):
+#             result = pipe(clean_text)[0]
+#             idx = int(result['label'].split('_')[1])
+
+#             if idx == 0:
+#                 sentiment = "😐 Neutral"
+#                 color = "gray"
+#             elif idx == 1:
+#                 sentiment = "😊 Positive"
+#                 color = "green"
+#             else:
+#                 sentiment = "😠 Negative"
+#                 color = "red"
+
+#             st.markdown(f"### Prediction: <span style='color:{color}'>{sentiment}</span>", unsafe_allow_html=True)
+
 import streamlit as st
 from transformers import pipeline
 import re
@@ -34,7 +109,10 @@ set_background("New3.jpg")
 
 # Title and Description
 st.title("📊 Telugu Sentiment Analysis")
-st.markdown("Analyze the sentiment (Positive, Negative, Neutral) of a given **Telugu** sentence using a fine-tuned BERT model.")
+st.markdown(
+    "<h4 style='color:#1affff;'>Analyze the sentiment (Positive, Negative, Neutral) of a given <b>Telugu</b> sentence using a fine-tuned BERT model.</h4>",
+    unsafe_allow_html=True
+)
 
 # Load the model pipeline
 @st.cache_resource