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app.py

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+import streamlit as st
+from keybert import KeyBERT
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+
+# 🔧 Must be first Streamlit command
+st.set_page_config(page_title="Keyword & Summary Bot", page_icon="🧠")
+
+# 📦 Load models only once
+@st.cache_resource
+def load_models():
+    kw_model = KeyBERT(SentenceTransformer('all-MiniLM-L6-v2'))
+    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+    return kw_model, summarizer
+
+kw_model, summarizer = load_models()
+
+# 🧠 UI
+st.title("🤖 NLP Assistant: Keyword Extractor & Summarizer")
+st.write("Welcome! Select a task below and enter your text to get smart results.")
+
+# 🧭 Task Selection
+task = st.selectbox("Choose your task:", ["Select task", "Keyword Extraction", "Text Summarization"])
+
+# ✏️ User Input
+user_input = st.text_area("Enter your text here:")
+
+# 🚀 Submit Button
+if st.button("Submit") and user_input.strip():
+
+    # 🔑 Keyword Extraction
+    if task == "Keyword Extraction":
+        keywords = kw_model.extract_keywords(
+            user_input,
+            keyphrase_ngram_range=(1, 2),
+            stop_words='english',
+            top_n=5
+        )
+        keyword_list = [kw[0] for kw in keywords]
+        st.success(f"🔑 Keywords: {', '.join(keyword_list)}")
+
+    # 📃 Text Summarization
+    elif task == "Text Summarization":
+        if len(user_input.split()) < 50:
+            st.warning("⚠️ Enter a longer paragraph (at least 50 words) for better summarization.")
+        else:
+            summary = summarizer(
+                user_input,
+                max_length=80,
+                min_length=20,
+                do_sample=False
+            )
+            st.success(f"📃 Summary: {summary[0]['summary_text']}")
+
+    else:
+        st.warning("⚠️ Please select a task to perform.")

pyvenv.cfg

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+home = C:\Users\bhava\AppData\Local\Programs\Python\Python310
+include-system-site-packages = false
+version = 3.10.0