Update app.py

app.py

@@ -1,62 +1,30 @@
-import os
+import spacy
+import pytextrank
 import streamlit as st
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.chains import RetrievalQA
-from langchain_groq import ChatGroq  # NEW import
 
-# -------------------------------
-# Sidebar for API key input
-# -------------------------------
-st.set_page_config(page_title="Groq PDF Chatbot")
-st.title("📄 Chat with your PDF using Groq + LLaMA3")
+# Load NLP model and add TextRank once
+nlp = spacy.load("en_core_web_lg")
+nlp.add_pipe("textrank")
 
-api_key = st.sidebar.text_input("🔑 Enter your Groq API Key", type="password")
-if not api_key:
-    st.warning("Please enter your Groq API key in the sidebar.")
-    st.stop()
+def summarize_text(input_text):
+    doc = nlp(input_text)
+    summary = "\n".join([f"• {sent.text}" for sent in doc._.textrank.summary(limit_phrases=2, limit_sentences=2)])
+    return summary
 
-os.environ["GROQ_API_KEY"] = api_key
+def main():
+    st.title("TextRank Text Summarizer")
+    st.write("This app generates a concise summary from your input text using TextRank.")
 
-# -------------------------------
-# Load and split the PDF
-# -------------------------------
-pdf_path = "docs/acca.pdf"  # Make sure this file is in your Space
-loader = PyPDFLoader(pdf_path)
-pages = loader.load()
+    input_text = st.text_area("Enter the text you want to summarize:", height=300)
 
-text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-docs = text_splitter.split_documents(pages)
+    if st.button("Summarize"):
+        if input_text.strip():
+            with st.spinner("Generating summary..."):
+                summary = summarize_text(input_text)
+                st.subheader("Summary:")
+                st.write(summary)
+        else:
+            st.warning("Please enter some text to summarize.")
 
-# -------------------------------
-# Vector store using FAISS
-# -------------------------------
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-vectorstore = FAISS.from_documents(docs, embeddings)
-
-# -------------------------------
-# Groq LLM setup
-# -------------------------------
-llm = ChatGroq(
-    temperature=0.7,
-    model_name="llama3-8b-8192",  # Make sure to use correct lowercase name
-    groq_api_key=api_key
-)
-
-qa_chain = RetrievalQA.from_chain_type(
-    llm=llm,
-    retriever=vectorstore.as_retriever(),
-    return_source_documents=True
-)
-
-# -------------------------------
-# User input and response
-# -------------------------------
-query = st.text_input("Ask a question based on the PDF:")
-if query:
-    with st.spinner("Generating answer..."):
-        result = qa_chain.invoke(query)
-        st.subheader("📌 Answer")
-        st.write(result["result"])
+if __name__ == "__main__":
+    main()