Create app.py

app.py

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+import os
+import faiss
+import numpy as np
+import requests
+import gradio as gr
+from openai import OpenAI
+from pypdf import PdfReader
+from sentence_transformers import SentenceTransformer
+css = """
+
+/* =========================
+   TELECOM ENTERPRISE THEME
+   Magenta AI Chatbot UI
+   ========================= */
+
+/* Background */
+
+body {
+    background: linear-gradient(135deg, #0B0B10, #141420);
+    font-family: "Segoe UI", Roboto, Arial;
+    color: white;
+}
+
+/* Main container */
+.gradio-container {
+    max-width: 1100px !important;
+    margin: auto;
+    padding: 20px;
+}
+
+/* Header / Title */
+h1, h2, h3 {
+    color: #E20074;
+    font-weight: 700;
+    letter-spacing: 0.5px;
+}
+
+/* Chat container */
+.chatbot, .gradio-chatbot {
+    background: rgba(28, 28, 37, 0.6);
+    border: 1px solid rgba(226, 0, 116, 0.2);
+    border-radius: 18px;
+    backdrop-filter: blur(14px);
+    padding: 10px;
+}
+
+/* USER MESSAGE */
+.message.user {
+    background: linear-gradient(135deg, #E20074, #b0005a);
+    color: white;
+    border-radius: 16px;
+    padding: 12px;
+    box-shadow: 0 6px 20px rgba(226, 0, 116, 0.3);
+    transition: 0.25s ease;
+}
+
+.message.user:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 10px 30px rgba(226, 0, 116, 0.5);
+}
+
+/* BOT MESSAGE */
+.message.bot {
+    background: rgba(255, 255, 255, 0.06);
+    color: white;
+    border-radius: 16px;
+    padding: 12px;
+    border-left: 3px solid #E20074;
+    transition: 0.25s ease;
+}
+
+.message.bot:hover {
+    transform: translateY(-2px);
+}
+
+/* INPUT BOX */
+textarea {
+    background: rgba(255,255,255,0.05) !important;
+    border: 1px solid rgba(226, 0, 116, 0.3) !important;
+    border-radius: 12px !important;
+    color: white !important;
+    padding: 12px !important;
+}
+
+/* BUTTONS */
+button {
+    background: #E20074 !important;
+    color: white !important;
+    border-radius: 10px !important;
+    border: none !important;
+    font-weight: 600;
+    transition: all 0.3s ease;
+}
+
+button:hover {
+    background: #ff2d9a !important;
+    transform: translateY(-2px);
+    box-shadow: 0 10px 25px rgba(226, 0, 116, 0.4);
+}
+
+/* INPUT FOCUS EFFECT */
+textarea:focus {
+    outline: none !important;
+    border: 1px solid #E20074 !important;
+    box-shadow: 0 0 15px rgba(226, 0, 116, 0.4);
+}
+
+/* Card style panels (future UI elements) */
+.card {
+    background: rgba(28, 28, 37, 0.7);
+    border: 1px solid rgba(226, 0, 116, 0.2);
+    border-radius: 16px;
+    padding: 15px;
+    transition: 0.3s;
+}
+
+.card:hover {
+    transform: scale(1.02);
+    box-shadow: 0 10px 30px rgba(0,0,0,0.4);
+}
+
+/* Scrollbar */
+::-webkit-scrollbar {
+    width: 8px;
+}
+
+::-webkit-scrollbar-thumb {
+    background: #E20074;
+    border-radius: 10px;
+}
+"""
+
+
+# Globals (shared state in Gradio)
+embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+index = None
+chunks = []
+
+# Add after globals:
+# Session memory
+
+chat_history = []
+
+def chat(user_input, history):
+    global chat_history
+    
+    # Build full context (PDF + conversation history)
+full_context = "\n".join([
+    f"User: {h['user']}\nBot: {h['bot']}"
+    for h in chat_history[-5:]
+]) if chat_history else "" 
+answer = generate_answer(user_input, full_context)
+    
+    # Store in memory
+    chat_history.append({"user": user_input, "bot": answer})
+    
+    # Update UI history
+    new_history = history + [
+        {"role": "user", "content": user_input},
+        {"role": "assistant", "content": answer}
+    ]
+    
+    return new_history, new_history
+
+def generate_answer(query, conversation_context=""):
+    if index is None:
+        return "⚠️ Please load a PDF first."
+    
+    rag_context = retrieve(query)
+    rag_text = "\n\n".join(rag_context)
+    
+    # ✅ Combine RAG + Conversation Memory
+    full_prompt = f"""You are a smart financial AI assistant that remembers conversations.
+
+Previous conversation:
+{conversation_context}
+
+PDF Context (use ONLY this for facts):
+{rag_text}
+
+Question: {query}
+
+Respond naturally and helpfully, referencing past discussion when relevant."""
+    
+    response = client.chat.completions.create(
+        model="llama-3.1-8b-instant",
+        messages=[{"role": "user", "content": full_prompt}],
+        temperature=0.7,
+        max_tokens=600
+    )
+    return response.choices[0].message.content
+
+# Groq client with HF Secrets
+client = OpenAI(
+    api_key=os.getenv("GROQ_API_KEY"),
+    base_url="https://api.groq.com/openai/v1",
+)
+
+def convert_drive_link(link):
+    try:
+        file_id = link.split("/d/")[1].split("/")[0]
+        return f"https://drive.google.com/uc?id={file_id}"
+    except:
+        return link
+
+def load_pdf_from_link(link):
+    global index, chunks
+    url = convert_drive_link(link)
+    PDF_PATH = "temp.pdf"
+    response = requests.get(url)
+    with open(PDF_PATH, "wb") as f:
+        f.write(response.content)
+    
+    reader = PdfReader(PDF_PATH)
+    texts = [page.extract_text() for page in reader.pages if page.extract_text()]
+    
+    # Chunking
+    chunks = []
+    for t in texts:
+        words = t.split()
+        for i in range(0, len(words), 500):
+            chunks.append(" ".join(words[i:i+500]))
+    
+    # Embeddings + FAISS
+    embeddings = embed_model.encode(chunks)
+    dim = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dim)
+    index.add(np.array(embeddings).astype('float32'))
+    
+    return f"✅ PDF loaded! {len(chunks)} chunks created."
+
+def retrieve(query, k=3):
+    if index is None:
+        return []
+    q_emb = embed_model.encode([query])
+    distances, indices = index.search(np.array(q_emb).astype('float32'), k)
+    return [chunks[i] for i in indices[0]]
+    
+# ... (keep all previous code until chat function)
+# UI (replace entirely):
+with gr.Blocks(title="Finance RAG", css=css) as app:
+    gr.Markdown("# 📊 Dynamic Finance RAG Chatbot")
+    
+    with gr.Row():
+        link_input = gr.Textbox(label="📎 Google Drive PDF Link", placeholder="https://drive.google.com/file/d/...")
+        load_btn = gr.Button("📥 Load PDF", variant="primary")
+    
+    status = gr.Textbox(label="Status", interactive=False)
+    
+    chatbot = gr.Chatbot(height=500)
+    msg = gr.Textbox(
+        label="💬 Ask about the PDF", 
+        placeholder="What are the key financial metrics?",
+        container=True
+    )
+    
+    # Events
+    load_btn.click(load_pdf_from_link, inputs=link_input, outputs=status)
+    msg.submit(chat, inputs=[msg, chatbot], outputs=[chatbot, chatbot])
+    msg.submit(lambda: "", outputs=msg)
+
+if __name__ == "__main__":
+    app.launch()