Create app.py

app.py

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+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
+
+# ================== LOAD ENV ==================
+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 Hugging Face Secrets.")
+
+client = Groq(api_key=GROQ_API_KEY)
+
+# ================== GLOBAL VECTOR DB ==================
+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 uploaded file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+        tmp.write(file.read())
+        pdf_path = tmp.name
+
+    # Load PDF
+    loader = PyPDFLoader(pdf_path)
+    documents = loader.load()
+
+    # Split text into chunks
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=500,
+        chunk_overlap=100
+    )
+    docs = splitter.split_documents(documents)
+
+    # Create embeddings
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2"
+    )
+
+    # Create vector database
+    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.invoke(question)
+
+    context = "\n\n".join([doc.page_content for doc in docs])
+
+    prompt = f"""
+You are an intelligent assistant.
+Use ONLY the context below to answer the question.
+
+Context:
+{context}
+
+Question:
+{question}
+
+Answer:
+"""
+
+    return groq_llm(prompt)
+
+
+# ================== GRADIO UI ==================
+with gr.Blocks(title="📄 RAG PDF Question Answering App") as demo:
+    gr.Markdown("# 📄 RAG PDF Question Answering App")
+    gr.Markdown("Upload a PDF and ask questions about it.")
+
+    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")
+        answer = gr.Textbox(label="Answer", interactive=False)
+
+    process_btn.click(process_pdf, inputs=pdf_upload, outputs=status)
+    question.submit(ask_question, inputs=question, outputs=answer)
+
+demo.launch()