Delete app.py

app.py

@@ -1,73 +0,0 @@
-import os
-import gradio as gr
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from langchain.llms import HuggingFaceHub
-
-# LLM for summarization and glossary extraction
-llm = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature": 0.7, "max_length": 512})
-
-summary_cache = ""
-glossary_cache = ""
-retriever_chain = None
-
-def extract_text_and_summary(file):
-    global retriever_chain, summary_cache, glossary_cache
-
-    loader = PyPDFLoader(file.name)
-    docs = loader.load()
-
-    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    splits = splitter.split_documents(docs)
-    full_text = "\n".join([doc.page_content for doc in splits])
-
-    embeddings = HuggingFaceEmbeddings()
-    db = FAISS.from_documents(splits, embeddings)
-    retriever = db.as_retriever()
-    retriever_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
-
-    summary_prompt = f"Summarize this legal document:\n{full_text[:1500]}"
-    glossary_prompt = f"Extract and define legal terms from the document:\n{full_text[:1500]}"
-
-    summary_cache = llm(summary_prompt)[0]['generated_text']
-    glossary_cache = llm(glossary_prompt)[0]['generated_text']
-
-    # Save downloadable summary file
-    filename = "summary_output.txt"
-    with open(filename, "w", encoding="utf-8") as f:
-        f.write("=== Summary ===\n")
-        f.write(summary_cache + "\n\n")
-        f.write("=== Glossary ===\n")
-        f.write(glossary_cache + "\n")
-
-    return full_text, summary_cache, glossary_cache, filename
-
-def answer_custom_question(question):
-    if retriever_chain:
-        return retriever_chain.run(question)
-    return "Please upload and process a document first."
-
-with gr.Blocks() as demo:
-    gr.Markdown("## 🧾 Legal Document Summarizer Using LangChain")
-
-    with gr.Row():
-        file = gr.File(label="📁 Upload Legal PDF", file_types=[".pdf"])
-        process_btn = gr.Button("🔍 Extract & Summarize")
-
-    extracted_text = gr.Textbox(label="📄 Extracted Text", lines=10)
-    summary_output = gr.Textbox(label="📝 Summary", lines=5)
-    glossary_output = gr.Textbox(label="📘 Glossary", lines=5)
-    download_link = gr.File(label="⬇️ Download Summary")
-
-    with gr.Row():
-        user_question = gr.Textbox(label="❓ Ask a Custom Question")
-        custom_answer = gr.Textbox(label="🤖 AI Answer")
-        ask_btn = gr.Button("🧠 Get Answer")
-
-    process_btn.click(fn=extract_text_and_summary, inputs=file, outputs=[extracted_text, summary_output, glossary_output, download_link])
-    ask_btn.click(fn=answer_custom_question, inputs=user_question, outputs=custom_answer)
-
-demo.launch()