Create app.py

app.py

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+import os
+import gradio as gr
+import faiss
+import numpy as np
+import PyPDF2
+from sentence_transformers import SentenceTransformer
+import requests
+
+# 🔐 Use environment variable for Groq API key
+GROQ_API_KEY = os.getenv("GROQ_API_KEY", "")
+GROQ_MODEL = "llama3-8b-8192"
+
+# 🧠 Embedding Model
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# 🗃️ Vector DB
+dimension = 384
+index = faiss.IndexFlatL2(dimension)
+text_chunks = []
+
+def extract_text_from_pdf(pdf_file):
+    reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text() or ""
+    return text
+
+def chunk_text(text, chunk_size=500):
+    sentences = text.split('. ')
+    chunks, chunk = [], ""
+    for sentence in sentences:
+        if len(chunk) + len(sentence) < chunk_size:
+            chunk += sentence + ". "
+        else:
+            chunks.append(chunk.strip())
+            chunk = sentence + ". "
+    if chunk:
+        chunks.append(chunk.strip())
+    return chunks
+
+def embed_and_store(chunks):
+    global text_chunks
+    text_chunks = chunks
+    embeddings = embedding_model.encode(chunks)
+    index.add(np.array(embeddings))
+
+def retrieve_context(query, top_k=3):
+    query_vector = embedding_model.encode([query])
+    distances, indices = index.search(np.array(query_vector), top_k)
+    return "\n".join([text_chunks[i] for i in indices[0]])
+
+def format_prompt(context, question):
+    system_msg = "You are a helpful research assistant who answers questions using only the uploaded document."
+    user_msg = f"Document Context:\n{context}\n\nQuestion: {question}\nAnswer:"
+    return [{"role": "system", "content": system_msg},
+            {"role": "user", "content": user_msg}]
+
+def call_groq_api(messages):
+    url = "https://api.groq.com/openai/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    data = {
+        "model": GROQ_MODEL,
+        "messages": messages,
+        "temperature": 0.3
+    }
+    response = requests.post(url, headers=headers, json=data)
+    return response.json()['choices'][0]['message']['content']
+
+def upload_file(pdf):
+    text = extract_text_from_pdf(pdf)
+    chunks = chunk_text(text)
+    embed_and_store(chunks)
+    return "✅ Document processed. You may now ask questions."
+
+def answer_question(question):
+    if not text_chunks:
+        return "❌ Please upload and process a document first."
+    context = retrieve_context(question)
+    messages = format_prompt(context, question)
+    return call_groq_api(messages)
+
+with gr.Blocks() as rag_ui:
+    gr.Markdown("## 📄 RAG Assistant with LLaMA3 (Groq)")
+    
+    with gr.Row():
+        pdf_input = gr.File(label="Upload PDF")
+        upload_button = gr.Button("Process Document")
+    
+    status_output = gr.Textbox(label="Status")
+    upload_button.click(upload_file, inputs=pdf_input, outputs=status_output)
+
+    gr.Markdown("### ❓ Ask a Question from the Uploaded PDF")
+    question_input = gr.Textbox(label="Your Question")
+    answer_output = gr.Textbox(label="Answer", lines=5)
+
+    ask_button = gr.Button("Get Answer")
+    ask_button.click(answer_question, inputs=question_input, outputs=answer_output)
+
+rag_ui.launch()