Update app.py

app.py

@@ -4,21 +4,33 @@ from huggingface_hub import InferenceClient
 from sentence_transformers import SentenceTransformer
 import torch
 
-client = InferenceClient("deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
-
 def respond(message, history):
-
     top_results = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
     print(top_results)
-    messages = [{"role": "system", "content": "You are a friendly chatbot. You give people advice about what their dogs can eat. Base your response on the following information {top_results}. You resond in complete sentences"}]
 
-    if history: 
+    # ✅ Format context for LLM
+    if top_results:
+        formatted_info = "\n".join(f"- {chunk}" for chunk in top_results)
+        system_prompt = (
+            f"You are a friendly chatbot that gives advice about what dogs can eat.\n"
+            f"Use the following information to guide your response:\n{formatted_info}\n"
+            f"Respond in complete sentences and apply common sense. If the user asks about something not in the list, "
+            f"give a cautious answer and suggest checking with a vet."
+        )
+    else:
+        system_prompt = (
+            "You are a friendly chatbot that gives advice about what dogs can eat.\n"
+            "The user asked about a food not in your database. Respond cautiously and suggest checking with a vet."
+        )
+
+    messages = [{"role": "system", "content": system_prompt}]
+
+    if history:
         messages.extend(history)
 
     messages.append({"role": "user", "content": message})
 
-    response = client.chat_completion(messages, max_tokens = 100, temperature = 0.2) 
-    #connecting to llm, max caps response
+    response = client.chat_completion(messages, max_tokens=100, temperature=0.2)
     return response['choices'][0]['message']['content'].strip()
 
 print("hello world")