Update app.py

app.py

@@ -1,89 +1,72 @@
 import gradio as gr
-import chromadb
-from sentence_transformers import SentenceTransformer
 from llama_cpp import Llama
 
-# ✅ Initialize ChromaDB
-chroma_client = chromadb.PersistentClient(path="./chromadb_store")
-collection = chroma_client.get_or_create_collection(name="curly_strings_knowledge")
-
-# ✅ Load Local Embedding Model
-embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-
-# ✅ Curly Strings Knowledge (Stored in ChromaDB as Vectors)
-knowledge_base = [
-    {"id": "song_list", "text": """
-    Curly Strings is an Estonian folk band known for blending traditional and modern sounds.
-    Here are some of their popular songs:
-    1. Kalakesed
-    2. Kus mu süda on ...
-    3. Vitsalaul
-    4. Viimases jaamas
-    5. Salaja
-    6. Üle ilma
-    7. Šveits
-    8. Kallimale
-    9. Üksteist peab hoidma
-    10. Suuda öelda ei
-    """},
-    {"id": "related_artists", "text": """
-    If you enjoy Curly Strings, you might also like:
-    - Trad.Attack!
-    - Eesti Raadio laululapsed
-    - Körsikud
-    - Karl-Erik Taukar
-    - Dag
-    """},
-    {"id": "background", "text": """
-    Curly Strings started in Estonia and became famous for their unique blend of folk and contemporary music.
-    They often perform at international festivals and are known for their emotional and poetic lyrics.
-    """}
-]
-
-# ✅ Store Knowledge in ChromaDB (If Not Already Stored)
-existing_data = collection.get()
-if not existing_data["ids"]:
-    for item in knowledge_base:
-        embedding = embedder.encode(item["text"]).tolist()
-        collection.add(documents=[item["text"]], embeddings=[embedding], ids=[item["id"]])
-
-# ✅ Load Llama Model
+# Load the Llama model
 llm = Llama.from_pretrained(
     repo_id="krishna195/second_guff",
     filename="unsloth.Q4_K_M.gguf",
 )
 
-# ✅ Function to Retrieve Relevant Knowledge (Fixed Nested List Issue)
-def retrieve_context(query):
-    query_embedding = embedder.encode(query).tolist()
-    results = collection.query(query_embeddings=[query_embedding], n_results=2)
-
-    # Flatten nested lists and ensure only strings are returned
-    retrieved_texts = [doc for sublist in results.get("documents", []) for doc in sublist if isinstance(doc, str)]
-    
-    return "\n".join(retrieved_texts) if retrieved_texts else "No relevant data found."
-
-# ✅ Chatbot Function with ChromaDB-RAG
+# Define the chatbot function
 def chatbot_response(user_input):
-    context = retrieve_context(user_input)  # Retrieve relevant info from ChromaDB
-    
-    messages = [
-        {"role": "system", "content": "Use the knowledge retrieved to answer the user’s question."},
-        {"role": "user", "content": user_input},
-        {"role": "assistant", "content": f"Retrieved Context:\n{context}"},
-    ]
+    # System instructions
+    system_prompt = """
+    You are a chatbot specializing in recommending songs by the Estonian folk band **Curly Strings**. 
+    based on this anw the question and give the link of the source 
+    ## 🎵 **Song List**
+    Here are some songs by Curly Strings:
+    1. **Kalakesed**
+    2. **Kus mu süda on ...**
+    3. **Vitsalaul**
+    4. **Viimases jaamas**
+    5. **Salaja**
+    6. **Üle ilma**
+    7. **Šveits**
+    8. **Kallimale**
+    9. **Üksteist peab hoidma**
+    10. **Suuda öelda ei**
+    11. **Annan käe**
+    12. **Tulbid ja Bonsai**
+    13. **Tüdruk Pika Kleidiga**
+    14. **Armasta mind (feat. Vaiko Eplik)**
+    15. **Minu, Pets, Margus ja Priit**
+    16. **Kauges külas**
+    17. **Tule ja jää**
+    18. **Kuutõbine**
+    19. **Omaenese ilus ja veas**
+    20. **Pulmad**
+    21. **Pillimeeste laul**
+    22. **Tehke ruumi!**
+    ## 🎤 **Related Artists**
+    If you enjoy Curly Strings, you might also like:
+    - **Trad.Attack!**
+    - **Eesti Raadio laululapsed**
+    - **Körsikud**
+    - **Karl-Erik Taukar**
+    - **Dag**
+    - **Sadamasild**
+    - **Kruuv**
+    - **Smilers**
+    - **Mari Jürjens**
+    - **Terminaator**
+    ---
+    """
 
+    # Generate response from Llama model
     response = llm.create_chat_completion(
-        messages=messages,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_input}
+        ],
         temperature=0.5,
-        max_tokens=500,
+        max_tokens=1000,  # Increased for better answers
         top_p=0.9,
         frequency_penalty=0.8,
     )
 
     return response["choices"][0]["message"]["content"].strip()
 
-# ✅ Gradio UI
+# Create Gradio interface
 iface = gr.Interface(
     fn=chatbot_response,
     inputs=gr.Textbox(placeholder="Ask me about Curly Strings..."),
@@ -93,5 +76,5 @@ iface = gr.Interface(
     theme="compact",
 )
 
-# ✅ Launch App
-iface.launch()
+# Launch the Gradio app
+iface.launch()