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app.py

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+import streamlit as st
+from transformers import pipeline
+from transformers.utils.logging import set_verbosity_error
+
+# Silence warnings
+set_verbosity_error()
+
+# Page Config
+st.set_page_config(page_title="Text Summarizer & QnA", layout="centered")
+
+# Custom Style
+st.markdown(
+    """
+    <style>
+        html, body, [data-testid="stAppViewContainer"], [data-testid="stApp"] {
+            background-color: #E8F0FE !important;
+        }
+        [data-testid="stHeader"] {
+            background: rgba(0,0,0,0);
+        }
+        [data-testid="stSidebar"] {
+            background-color: #E8F0FE !important;
+        }
+        .title {
+            text-align: center;
+            font-size: 2.2rem;
+            color: #1E3A8A;
+            font-weight: 700;
+            margin-top: 1rem;
+            margin-bottom: 0.5rem;
+        }
+        .subheader {
+            text-align: center;
+            color: #475569;
+            font-size: 1rem;
+            margin-bottom: 2rem;
+        }
+        .stTextArea textarea {
+            border-radius: 10px !important;
+        }
+        .stButton>button {
+            background-color: #1E40AF;
+            color: white;
+            border: none;
+            border-radius: 8px;
+            padding: 0.6rem 1.2rem;
+            font-weight: 600;
+            transition: 0.3s;
+        }
+        .stButton>button:hover {
+            background-color: #2563EB;
+        }
+        .output-box {
+            background-color: #C7D2FE;
+            padding: 1rem;
+            border-radius: 10px;
+            color: #1E3A8A;
+            font-weight: 500;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Title Section
+st.markdown("<div class='title'>🧠 Smart Text Summarizer & QnA Assistant</div>", unsafe_allow_html=True)
+st.markdown("<div class='subheader'>Summarize long text and ask questions about it easily!</div>", unsafe_allow_html=True)
+
+# Load Models
+@st.cache_resource
+def load_pipelines():
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    refiner = pipeline("summarization", model="facebook/bart-large")
+    qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
+    return summarizer, refiner, qa_pipeline
+
+summarizer, refiner, qa_pipeline = load_pipelines()
+
+# Summarization
+text_to_summarize = st.text_area("Enter text to summarize:", height=200)
+length = st.radio("Select summary length:", ["short", "medium", "long"], horizontal=True)
+
+# Define summary length dynamically
+length_settings = {
+    "short": {"min_length": 30, "max_length": 80},
+    "medium": {"min_length": 80, "max_length": 160},
+    "long": {"min_length": 160, "max_length": 300},
+}
+
+if st.button("Summarize"):
+    if text_to_summarize.strip():
+        with st.spinner("Generating summary..."):
+            try:
+                # Apply variable summary lengths
+                params = length_settings[length]
+                raw_summary = summarizer(text_to_summarize, **params)
+                summary = raw_summary[0]["summary_text"]
+
+                # Optionally refine
+                refined_summary = refiner(summary, min_length=20, max_length=150)[0]["summary_text"]
+
+                st.session_state["summary"] = refined_summary
+
+                st.markdown("### 🔹 Generated Summary:")
+                st.markdown(f"<div class='output-box'>{refined_summary}</div>", unsafe_allow_html=True)
+            except Exception as e:
+                st.error(f"Error: {e}")
+    else:
+        st.warning("Please enter some text to summarize.")
+
+# Q&A Section
+if "summary" in st.session_state:
+    st.markdown("---")
+    st.subheader("❓ Ask a question about the summary:")
+    question = st.text_input("Type your question here:")
+
+    if st.button("Get Answer"):
+        if question.strip():
+            with st.spinner("Finding answer..."):
+                qa_result = qa_pipeline(question=question, context=st.session_state["summary"])
+            st.markdown("### 🔹 Answer:")
+            st.markdown(f"<div class='output-box'>{qa_result['answer']}</div>", unsafe_allow_html=True)
+        else:
+            st.warning("Please enter a question.")

main.py

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+from transformers import pipeline
+from langchain_huggingface import HuggingFacePipeline
+from langchain.prompts import PromptTemplate
+from transformers.utils.logging import set_verbosity_error
+
+set_verbosity_error()
+
+summarization_pipeline = pipeline("summarization", model="facebook/bart-large-cnn", device=0)
+summarizer = HuggingFacePipeline(pipeline=summarization_pipeline)
+
+refinement_pipeline = pipeline("summarization", model="facebook/bart-large", device=0)
+refiner = HuggingFacePipeline(pipeline=refinement_pipeline)
+
+qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2", device=0)
+
+summary_template = PromptTemplate.from_template("Summarize the following text in a {length} way:\n\n{text}")
+
+summarization_chain = summary_template | summarizer | refiner
+
+text_to_summarize = input("\nEnter text to summarize:\n")
+length = input("\nEnter the length (short/medium/long): ")
+
+summary = summarization_chain.invoke({"text": text_to_summarize, "length": length})
+
+print("\n🔹 **Generated Summary:**")
+print(summary)
+
+while True:
+    question = input("\nAsk a question about the summary (or type 'exit' to stop):\n")
+    if question.lower() == "exit":
+        break
+
+    qa_result = qa_pipeline(question=question, context=summary)
+
+    print("\n🔹 **Answer:**")
+    print(qa_result["answer"])

requirements.txt

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+transformers
+langchain
+langchain-huggingface