Create app.py

app.py

@@ -0,0 +1,46 @@
+import streamlit as st
+from transformers import pipeline
+import re
+
+# Title and Description
+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 the model pipeline
+@st.cache_resource
+def load_pipeline():
+    return pipeline("text-classification", model="Adityaganesh/Telugu_Sentiment_Analysis")
+
+pipe = load_pipeline()
+
+# Optional: Text Preprocessing (basic cleaning)
+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)
+            st.markdown(f"**Confidence:** `{result['score']:.2f}`")