Create app.py

app.py

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+import os
+import requests
+import gradio as gr
+from groq import Groq
+
+# LangChain components
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+
+# --- 1. SETUP & API KEYS ---
+# Hugging Face uses os.getenv to read secrets
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+client = Groq(api_key=GROQ_API_KEY)
+
+# Add your links here
+GDRIVE_LINKS = [
+    "https://drive.google.com/file/d/12bS7b-Q3qdbnwCRcTynXjMj1IyKFzBJl/view?usp=sharing"
+]
+
+def download_gdrive_pdf(url, output_path):
+    try:
+        file_id = url.split('/')[-2]
+        download_url = f'https://drive.google.com/uc?export=download&id={file_id}'
+        response = requests.get(download_url)
+        if response.status_code == 200:
+            with open(output_path, 'wb') as f:
+                f.write(response.content)
+            return True
+    except:
+        return False
+    return False
+
+# --- 2. KNOWLEDGE BASE INITIALIZATION ---
+print("Initializing Knowledge Base...")
+all_chunks = []
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=600, chunk_overlap=150)
+
+for i, link in enumerate(GDRIVE_LINKS):
+    filename = f"doc_{i}.pdf"
+    if download_gdrive_pdf(link, filename):
+        loader = PyPDFLoader(filename)
+        all_chunks.extend(text_splitter.split_documents(loader.load()))
+
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+vector_db = FAISS.from_documents(all_chunks, embeddings)
+print("System Ready.")
+
+# --- 3. RAG LOGIC ---
+def respond(message, history):
+    docs = vector_db.similarity_search(message, k=5)
+    context = "\n\n".join([doc.page_content for doc in docs])
+    
+    system_prompt = f"""
+    You are a professional Knowledge Assistant. 
+    1. Answer the question using ONLY the provided context.
+    2. If the answer is not in the context, say: "I'm sorry, I couldn't find that in the documents."
+    3. Be concise and factual.
+    
+    CONTEXT:
+    {context}
+    """
+
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": message},
+        ],
+        model="llama-3.3-70b-versatile",
+        temperature=0.1,
+    )
+    return chat_completion.choices[0].message.content
+
+# --- 4. MODERN UI ---
+custom_css = """
+footer {visibility: hidden}
+.gradio-container { background-color: #fcfcfc; }
+#chatbot-container { border-radius: 12px; box-shadow: 0 2px 10px rgba(0,0,0,0.05); }
+"""
+
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo"), css=custom_css) as demo:
+    gr.Markdown("# 📑 Enterprise Knowledge Chat")
+    gr.Markdown("Ask questions based on your specialized document library.")
+    
+    chatbot = gr.ChatInterface(
+        fn=respond,
+        chatbot=gr.Chatbot(height=550, elem_id="chatbot-container"),
+        textbox=gr.Textbox(placeholder="Ask me anything...", container=False, scale=7),
+        type="messages"
+    )
+
+if __name__ == "__main__":
+    demo.launch()