Create app.py

app.py

@@ -0,0 +1,70 @@
+import streamlit as st
+from transformers import pipeline
+from keybert import KeyBERT
+
+# ----------------------------
+# PAGE CONFIG
+# ----------------------------
+st.set_page_config(page_title="📝 Smart Text Analyzer", layout="wide")
+st.title("📝 Smart Text Analyzer App")
+st.markdown("Paste your text below and explore summaries, sentiment, and keywords.")
+
+# ----------------------------
+# LOAD MODELS
+# ----------------------------
+@st.cache_resource
+def load_models():
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    sentiment_analyzer = pipeline("sentiment-analysis")
+    kw_model = KeyBERT()
+    return summarizer, sentiment_analyzer, kw_model
+
+summarizer, sentiment_analyzer, kw_model = load_models()
+
+# ----------------------------
+# USER INPUT
+# ----------------------------
+text_input = st.text_area("Enter your text here:", height=200)
+summary_length = st.radio("Select summary length:", ["Short", "Medium", "Long"])
+
+length_map = {"Short": 50, "Medium": 100, "Long": 150}
+
+# ----------------------------
+# ACTION
+# ----------------------------
+if st.button("Analyze Text"):
+    if text_input.strip():
+        with st.spinner("Processing..."):
+
+            # Summarization
+            summary = summarizer(
+                text_input,
+                max_length=length_map[summary_length],
+                min_length=25,
+                do_sample=False
+            )[0]["summary_text"]
+
+            # Sentiment
+            sentiment = sentiment_analyzer(text_input[:512])[0]
+
+            # Keywords
+            keywords = kw_model.extract_keywords(text_input, keyphrase_ngram_range=(1,2), stop_words='english', top_n=5)
+            keywords = [kw[0] for kw in keywords]
+
+        # ----------------------------
+        # OUTPUTS
+        # ----------------------------
+        st.subheader("📌 Summary:")
+        st.write(summary)
+
+        st.subheader("😊 Sentiment Analysis:")
+        st.write(f"**Label:** {sentiment['label']} | **Score:** {sentiment['score']:.2f}")
+
+        st.subheader("🔑 Top Keywords:")
+        st.write(", ".join(keywords))
+
+        # Download Option
+        st.download_button("💾 Download Summary", data=summary, file_name="summary.txt")
+
+    else:
+        st.warning("⚠️ Please enter some text first!")