reboot to lesson 10 and made changes

app.py

@@ -3,75 +3,44 @@ from huggingface_hub import InferenceClient
 
 client = InferenceClient("Qwen/Qwen2.5-7B-Instruct")
 
-songs = [
-    {
-        "title": "Blinding Lights",
-        "artist": "The Weeknd",
-        "description": "An energetic pop song with retro synth vibes, great for night drives."
-    },
-    {
-        "title": "Someone Like You",
-        "artist": "Adele",
-        "description": "A deeply emotional ballad about heartbreak and lost love."
-    },
-    {
-        "title": "Lose Yourself",
-        "artist": "Eminem",
-        "description": "An intense and motivational rap song about seizing opportunities."
-    },
-    {
-        "title": "Clair de Lune",
-        "artist": "Debussy",
-        "description": "A calm and soothing classical piano piece, perfect for relaxation."
-    },
-]
-
-from sentence_transformers import SentenceTransformer, util
-
-embedder = SentenceTransformer("all-MiniLM-L6-v2")
-
-song_texts = [s["description"] for s in songs]
-song_embeddings = embedder.encode(song_texts, convert_to_tensor=True)
-
-def retrieve_songs(query, top_k=2):
-    query_embedding = embedder.encode(query, convert_to_tensor=True)
-    scores = util.cos_sim(query_embedding, song_embeddings)[0]
-
-    top_results = scores.topk(k=top_k)
-
-    results = []
-    for idx in top_results.indices:
-        results.append(songs[idx])
-
-    return results
-
-def generate_response(query, retrieved_songs):
-    context = "\n".join([
-        f"{s['title']} by {s['artist']}: {s['description']}"
-        for s in retrieved_songs
-    ])
-
-    prompt = f"""
-User preference: {query}
-
-Here are some songs:
-{context}
-
-Recommend one or two songs and explain why they fit the user's mood.
-"""
-
-    output = generator(prompt, max_length=150, num_return_sequences=1)
-    return output[0]["generated_text"]
-
-    print("🎵 Music Bot (RAG powered). Type 'exit' to quit.\n")
-
-while True:
-    user_input = input("You: ")
-
-    if user_input.lower() == "exit":
-        break
-
-    retrieved = retrieve_songs(user_input)
-    response = generate_response(user_input, retrieved)
-
-    print("\nBot:", response, "\n")
+def respond(message, history):
+    
+    messages = [{
+    "role": "system",
+    "content": (
+        "You are a warm, friendly chatbot who believes all users are sharks. "
+        "Always include a short, uplifting positive affirmation in every response. "
+        "Keep responses supportive, encouraging, and gentle."
+    )
+}]
+
+def add_affirmation_if_missing(text):
+    affirmations = [
+        "Remember, you are capable and strong.",
+        "You deserve kindness and good things.",
+        "You’re doing a great job, even on tough days.",
+        "You are a necessary part of the world."
+    ]
+    
+    if any(phrase in text.lower() for phrase in ["you are", "you're", "you’ve got", "you can"]):
+        return text
+    else:
+        return text + " " + affirmations[0]
+    if history:
+    for user_msg, bot_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": bot_msg})
+        
+    messages.append({"role": "user", "content": message})
+    
+    response = client.chat_completion(
+        messages,
+        max_tokens=110
+    )
+    
+reply = response.choices[0].message.content.strip()
+return add_affirmation_if_missing(reply)
+
+chatbot = gr.ChatInterface(respond)
+
+chatbot.launch()