Create app.py

app.py

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+import gradio as gr
+from langchain_community.document_loaders import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from groq import Groq
+import os
+
+# === Config ===
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # Use environment variable for Groq API Key
+LLM_MODEL = "llama3-70b-8192"
+FILE_PATH = "./Estonia.txt"  # Use relative path for Hugging Face Space
+DB_DIR = "chroma_db"
+
+# === Load and Chunk Document ===
+def load_and_split(filepath):
+    loader = TextLoader(filepath, encoding="utf-8")
+    docs = loader.load()
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    chunks = splitter.split_documents(docs)
+    return chunks
+
+# === Create or Load Vector Store ===
+def get_vector_store(chunks):
+    embeddings = HuggingFaceEmbeddings(
+        model_name="all-MiniLM-L6-v2",
+        model_kwargs={"token": os.getenv("HF_TOKEN")}  # Use environment variable for HuggingFace token
+    )
+    vectordb = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=DB_DIR)
+    return vectordb
+
+def load_vector_store():
+    embeddings = HuggingFaceEmbeddings(
+        model_name="all-MiniLM-L6-v2",
+        model_kwargs={"token": os.getenv("HF_TOKEN")}  # Use environment variable for HuggingFace token
+    )
+    vectordb = Chroma(persist_directory=DB_DIR, embedding_function=embeddings)
+    return vectordb
+
+# === Query LLaMA via Groq ===
+def query_llama(context, question):
+    client = Groq(api_key=GROQ_API_KEY)
+
+    prompt = f"""Use the following context to answer the question:
+You're a cheerful, funky band member from Curly Strings 🎻🎤🎶. When fans ask you questions, respond with playful, short, and friendly answers — like you're chatting backstage after a show.
+
+👇 Here’s how to guide fans:
+- **🎵 Music**: If they ask for songs or albums, suggest a tune and drop **just one** of these links (rotate each time!):  
+  - [Our Music Page](https://www.curlystrings.ee/music/)  
+  - [Spotify](https://open.spotify.com/playlist/37i9dQZF1DZ06evO3XF2F2)  
+  - [Apple Music](https://music.apple.com/us/artist/curly-strings/888454075)  
+  - [YouTube](https://www.youtube.com/@CurlyStringsEstonia)
+
+- **🎫 Tickets & Shows**: If it’s about concerts or dates, suggest one link from:  
+  - [Tour Dates](https://www.curlystrings.ee/tour-dates/)  
+  - [BandsInTown](https://www.bandsintown.com/a/6429648-curly-strings)
+
+- **🛍️ Merch**: If they ask about merch, shirts, or goodies, share:  
+  - [Our Merch Store](https://www.311.ee/curly-strings)
+
+Important: Always suggest **only one** link at a time. Rotate the links so fans get different suggestions each time!
+
+\n\n{context}\n\nQuestion: {question}\nAnswer:"""
+
+    response = client.chat.completions.create(
+        model=LLM_MODEL,
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=300
+    )
+
+    return response.choices[0].message.content.strip()
+
+# === RAG Pipeline ===
+def rag_pipeline(query):
+    if not os.path.exists(DB_DIR):
+        chunks = load_and_split(FILE_PATH)
+        vectordb = get_vector_store(chunks)
+    else:
+        vectordb = load_vector_store()
+
+    retriever = vectordb.as_retriever(search_kwargs={"k": 4})
+    docs = retriever.invoke(query)
+    context = "\n\n".join([doc.page_content for doc in docs])
+    return query_llama(context, query)
+
+# === Gradio Interface ===
+def chat_with_bot(question):
+    try:
+        answer = rag_pipeline(question)
+        return answer
+    except Exception as e:
+        return f"Oops! Something went wrong: {e}"
+
+# Launch Gradio UI
+iface = gr.Interface(
+    fn=chat_with_bot,
+    inputs=gr.Textbox(lines=2, placeholder="Ask me anything about Curly Strings 🎻🎤"),
+    outputs="text",
+    title="🎶 Curly Strings Chatbot 🎶",
+    description="Talk to a cheerful band member! Ask about music, shows, or merch."
+)
+
+iface.launch()