Update app.py


app.py

@@ -1,116 +1,42 @@
-# import streamlit as st
-# from transformers import pipeline
-# import re
-
-# # Load the model
-# classifier = pipeline("text-classification", model="Mpavan45/Telugu_Sentimental_Analysis")
-
-# # CSS styling
-# st.markdown("""
-#     <style>
-#     .radium-title {
-#         font-size: 40px;
-#         text-align: center;
-#         color: #fff;
-#         padding: 10px;
-#         border-radius: 10px;
-#         background: linear-gradient(90deg, #ff416c, #ff4b2b);
-#         box-shadow: 0 0 20px #ff416c, 0 0 30px #ff4b2b;
-#     }
-#     .radium-label {
-#         font-size: 24px;
-#         font-weight: bold;
-#         color: white;
-#         padding: 10px;
-#         border-radius: 8px;
-#         background: linear-gradient(90deg, #36d1dc, #5b86e5);
-#         display: inline-block;
-#         margin-top: 10px;
-#     }
-#     </style>
-# """, unsafe_allow_html=True)
-
-# st.markdown('<div class="radium-title">Sentiment Analysis with BERT</div>', unsafe_allow_html=True)
-# st.write("This app uses a fine-tuned BERT model to classify **Telugu text** as Positive, Negative, or Neutral. English is not supported.")
-
-# # Emoji mapping
-# label_map = {
-#     "LABEL_0": ("Negative", "😞"),
-#     "LABEL_1": ("Neutral", "😐"),
-#     "LABEL_2": ("Positive", "😊")
-# }
-
-# # Telugu validation (at least 70% of letters must be Telugu)
-# def is_telugu_text(text):
-#     telugu_chars = re.findall(r'[\u0C00-\u0C7F]', text)
-#     total_chars = re.findall(r'\S', text)  # non-space chars
-#     if not total_chars:
-#         return False
-#     return len(telugu_chars) / len(total_chars) >= 0.7
-
-# # Session state init
-# if "text_input" not in st.session_state:
-#     st.session_state.text_input = ""
-# if "result_shown" not in st.session_state:
-#     st.session_state.result_shown = False
-# if "from_example" not in st.session_state:
-#     st.session_state.from_example = False
-
-# # Examples
-# st.subheader("Try one of the following examples:")
-# examples = [
-#     "ఈ ఆహారం చాలా చెడుగా ఉంది",
-#     "నాకు ఈ రోజు చాలా సంతోషంగా ఉంది",
-#     "నేను ఈ వార్తలకు చాలా బాధపడ్డాను",
-#     "ఈ చిత్రం నాకు చాలా భయంకరంగా ఉంది",
-#     "ఈ సెల్ఫీ చాలా అందంగా ఉంది",
-#     "ఈ వాతావరణం నాకు చాలా ఉష్ణంగా ఉంది"
-# ]
-
-# for i in range(0, len(examples), 2):
-#     cols = st.columns(2)
-#     for j in range(2):
-#         if i + j < len(examples):
-#             example = examples[i + j]
-#             if cols[j].button(example[:30] + "..."):
-#                 st.session_state.text_input = example
-#                 st.session_state.result_shown = True
-#                 st.session_state.from_example = True
-
-# # Text area
-# text_input = st.text_area("Enter text to analyze sentiment (Telugu only):", value=st.session_state.text_input, height=150)
-
-# # Reset if input changed
-# if st.session_state.from_example and text_input != st.session_state.text_input:
-#     st.session_state.from_example = False
-#     st.session_state.result_shown = False
-
-# # Analyze button
-# if st.button("Analyze Sentiment"):
-#     st.session_state.text_input = text_input
-#     st.session_state.result_shown = True
-#     st.session_state.from_example = False
-
-# # Predict only if valid Telugu
-# if st.session_state.result_shown:
-#     input_text = st.session_state.text_input.strip()
-#     if not input_text:
-#         st.warning("Please enter some text to analyze!")
-#         st.session_state.result_shown = False
-#     elif not is_telugu_text(input_text):
-#         st.error("Please enter valid **Telugu** text. English or other languages are not supported.")
-#         st.session_state.result_shown = False
-#     else:
-#         result = classifier(input_text)
-#         raw_label = result[0]['label']
-#         sentiment, emoji = label_map.get(raw_label, (raw_label, ""))
-#         st.markdown(f'<div class="radium-label">Sentiment: {sentiment} {emoji}</div>', unsafe_allow_html=True)
 import streamlit as st
 import re
 from transformers import pipeline
 
 # Load the model
 classifier = pipeline("text-classification", model="Mpavan45/Telugu_Sentimental_Analysis")
+st.markdown("""
+    <style>
+    /* Background image for the entire app */
+    .stApp {
+        background-image: url('https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/NUr5kRtYi71HClXecxPTf.jpeg'); /* Replace with your image URL */
+        background-size: cover;
+        background-position: center;
+        background-repeat: no-repeat;
+        background-attachment: fixed;
+    }
+    /* Radium title styling */
+    .radium-title {
+        font-size: 40px;
+        text-align: center;
+        color: #fff;
+        padding: 10px;
+        border-radius: 10px;
+        background: linear-gradient(90deg, #ff416c, #ff4b2b);
+        box-shadow: 0 0 20px #ff416c, 0 0 30px #ff4b2b;
+    }
+    /* Radium label styling */
+    .radium-label {
+        font-size: 24px;
+        font-weight: bold;
+        color: white;
+        padding: 10px;
+        border-radius: 8px;
+        background: linear-gradient(90deg, #36d1dc, #5b86e5);
+        display: inline-block;
+        margin-top: 10px;
+    }
+    </style>
+""", unsafe_allow_html=True)
 
 # CSS for radium effect
 st.markdown("""