Update app.py

app.py

@@ -10,7 +10,7 @@ client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 DEFAULT_MAX_TOKENS = 512
 DEFAULT_TEMPERATURE = 0.7
 DEFAULT_TOP_P = 0.95
-DEFAULT_SYSTEM_MESSAGE = "You are an expert in environmental psychology. Provide expert recommendations."
+DEFAULT_SYSTEM_MESSAGE = "You are an expert in environmental psychology. Provide expert recommendations addressing the user directly."
 
 def generate_recommendations(comfort, social_interaction, stressors, privacy, open_question, max_tokens=DEFAULT_MAX_TOKENS, temperature=DEFAULT_TEMPERATURE, top_p=DEFAULT_TOP_P, system_message=DEFAULT_SYSTEM_MESSAGE):
     # Construct the input message for the model with context
@@ -21,7 +21,7 @@ def generate_recommendations(comfort, social_interaction, stressors, privacy, op
                f"Environmental Stressors: {stressors}\n"
                f"Privacy and Personal Space: {privacy}\n"
                f"Open-ended Question: {open_question}\n"
-               f"Provide recommendations for improving the environment based on these ratings.")
+               f"Please provide personalized recommendations addressing the user directly.")
 
     # Generate recommendations using the Hugging Face model
     response = client.chat_completion(
@@ -32,6 +32,8 @@ def generate_recommendations(comfort, social_interaction, stressors, privacy, op
     )
 
     recommendations = response.choices[0].message['content']
+    # Convert the recommendations to address the user in first person
+    recommendations = recommendations.replace("You should", "I recommend that you")
     return recommendations
 
 def analyze_environmental_concerns(comfort, social_interaction, stressors, privacy, open_question):