Delete app.py

app.py

@@ -1,111 +0,0 @@
-import os
-import gradio as gr
-import tempfile
-from dotenv import load_dotenv
-
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-
-from groq import Groq
-
-# ================== ENVIRONMENT ==================
-load_dotenv()
-GROQ_API_KEY = os.getenv("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")
-
-if not GROQ_API_KEY:
-    raise ValueError("❌ GROQ_API_KEY not found. Please set it in environment variables.")
-
-client = Groq(api_key=GROQ_API_KEY)
-
-# ================== GLOBAL DATABASE ==================
-vector_db = None
-
-# ================== LLM FUNCTION ==================
-def groq_llm(prompt):
-    response = client.chat.completions.create(
-        model="llama-3.3-70b-versatile",
-        messages=[{"role": "user", "content": prompt}],
-    )
-    return response.choices[0].message.content
-
-# ================== PDF PROCESSING ==================
-def process_pdf(file):
-    global vector_db
-
-    if file is None:
-        return "❌ Please upload a PDF file."
-
-    # Save file temporarily
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
-        tmp.write(file)
-        pdf_path = tmp.name
-
-    # Load PDF
-    loader = PyPDFLoader(pdf_path)
-    documents = loader.load()
-
-    # Chunking
-    splitter = RecursiveCharacterTextSplitter(
-        chunk_size=500,
-        chunk_overlap=100
-    )
-    docs = splitter.split_documents(documents)
-
-    # Embeddings (open-source)
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2"
-    )
-
-    # Vector store
-    vector_db = FAISS.from_documents(docs, embeddings)
-
-    return f"✅ Document processed successfully! {len(docs)} chunks created."
-
-# ================== QUESTION ANSWERING ==================
-def ask_question(question):
-    global vector_db
-
-    if vector_db is None:
-        return "❌ Please upload and process a document first."
-
-    retriever = vector_db.as_retriever(search_kwargs={"k": 3})
-    docs = retriever.get_relevant_documents(question)
-
-    context = "\n\n".join([doc.page_content for doc in docs])
-
-    prompt = f"""
-You are an intelligent assistant. Use the following context to answer the user's question.
-
-Context:
-{context}
-
-Question:
-{question}
-
-Answer:
-"""
-
-    answer = groq_llm(prompt)
-    return answer
-
-# ================== GRADIO UI ==================
-with gr.Blocks(title="📄 RAG PDF Question Answering App") as demo:
-    gr.Markdown("## 📄 RAG (Retrieval-Augmented Generation) Application")
-    gr.Markdown("Upload a PDF document and ask questions about its content.")
-
-    with gr.Row():
-        pdf_upload = gr.File(label="Upload PDF", file_types=[".pdf"])
-        process_btn = gr.Button("📥 Process Document")
-
-    status = gr.Textbox(label="Status", interactive=False)
-
-    with gr.Row():
-        question = gr.Textbox(label="Ask a Question", placeholder="Type your question here...")
-        answer = gr.Textbox(label="Answer", interactive=False)
-
-    process_btn.click(fn=process_pdf, inputs=pdf_upload, outputs=status)
-    question.submit(fn=ask_question, inputs=question, outputs=answer)
-
-demo.launch()