Update app.py

app.py

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+import gradio as gr
+from PyPDF2 import PdfReader
+from sentence_transformers import SentenceTransformer
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import faiss
+import numpy as np
+import requests
+import os
+
+# Initialize embedder model
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Groq API config
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "your-groq-api-key")
+GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
+
+# Function: Extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    reader = PdfReader(pdf_file)
+    text = ""
+    for page in reader.pages:
+        page_text = page.extract_text()
+        if page_text:
+            text += page_text + "\n"
+    return text
+
+# Function: Split text into chunks
+def split_text_into_chunks(text, chunk_size=500, chunk_overlap=100):
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap
+    )
+    return splitter.split_text(text)
+
+# Function: Generate embeddings
+def generate_embeddings(chunks):
+    return embedder.encode(chunks, show_progress_bar=False)
+
+# Function: Build FAISS index
+def build_faiss_index(embeddings):
+    dim = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dim)
+    index.add(embeddings)
+    return index
+
+# Function: Retrieve top-k chunks from index
+def retrieve_chunks(query, index, chunks, top_k=3):
+    query_vector = embedder.encode([query])
+    D, I = index.search(np.array(query_vector), top_k)
+    return [chunks[i] for i in I[0]]
+
+# Function: Query Groq with context
+def query_groq_with_context(context, question):
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    data = {
+        "model": "llama3-8b-8192",
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": f"Context:\n{context}\n\nQuestion:\n{question}"}
+        ]
+    }
+    response = requests.post(GROQ_API_URL, headers=headers, json=data)
+    result = response.json()
+    return result['choices'][0]['message']['content']
+
+# Global state to store chunks and index
+state = {"chunks": None, "index": None}
+
+# Gradio Functions
+def process_pdf(pdf_file):
+    text = extract_text_from_pdf(pdf_file)
+    chunks = split_text_into_chunks(text)
+    embeddings = generate_embeddings(chunks)
+    index = build_faiss_index(np.array(embeddings))
+    state["chunks"] = chunks
+    state["index"] = index
+    return "✅ PDF processed. You can now ask questions."
+
+def answer_question(question):
+    if not state["chunks"] or not state["index"]:
+        return "❗Please upload and process a PDF first."
+    relevant_chunks = retrieve_chunks(question, state["index"], state["chunks"])
+    context = "\n\n".join(relevant_chunks)
+    return query_groq_with_context(context, question)
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 📄 RAG PDF Chatbot using Groq LLaMA 3")
+
+    with gr.Row():
+        pdf_input = gr.File(label="Upload PDF")
+        process_button = gr.Button("📥 Process PDF")
+    status = gr.Textbox(label="Status")
+
+    process_button.click(fn=process_pdf, inputs=pdf_input, outputs=status)
+
+    question = gr.Textbox(label="Ask a question about the PDF")
+    ask_button = gr.Button("🔍 Ask")
+    answer = gr.Textbox(label="Answer", lines=5)
+
+    ask_button.click(fn=answer_question, inputs=question, outputs=answer)
+
+demo.launch()