Update app.py

app.py

@@ -17,7 +17,7 @@ api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
 if api_token:
     login(token=api_token)
 else:
-    print("API token is not set in the environment variables.")
+    st.error("API token is not set in the environment variables.")
 
 # Define model links
 model_links = {
@@ -27,6 +27,9 @@ model_links = {
 # Set selected model
 selected_model = "HAH-2024-v0.1"
 
+# Display welcome message
+st.title("Welcome to the Diabetes Management Assistant")
+
 # Sidebar setup
 temp_values = st.sidebar.slider("Select a temperature value", 0.0, 1.0, (0.5))
 def reset_conversation():
@@ -37,7 +40,9 @@ st.sidebar.button("Reset Chat", on_click=reset_conversation)
 st.sidebar.write(f"You're now chatting with **{selected_model}**")
 st.sidebar.image("https://www.hmgaihub.com/untitled.png")
 
+# Function to load model
 def load_model(selected_model_name):
+    st.info("Loading the model, please wait...")
     model_name = model_links[selected_model_name]
 
     # Set a specific device
@@ -79,10 +84,10 @@ def load_model(selected_model_name):
     tokenizer = AutoTokenizer.from_pretrained(
         "mistralai/Mistral-7B-Instruct-v0.2", trust_remote_code=True
     )
+    st.success("Model is ready. Now we are ready!")
 
     return model, tokenizer
 
-
 # Load model and tokenizer
 model, tokenizer = load_model(selected_model)
 
@@ -98,28 +103,16 @@ if prompt := st.chat_input("Ask me anything about diabetes"):
     with st.chat_message("user"):
         st.markdown(prompt)
 
-    # Append user prompt to session state
     st.session_state.messages.append({"role": "user", "content": prompt})
 
-    # Structured instructions as used in your Colab example
     instructions = """
     Act as a highly knowledgeable endocrinology doctor with expertise in explaining complex medical information in an understandable way to patients who do not have a medical background. Your responses should not only convey empathy and care but also demonstrate a high level of medical accuracy and reliability.
-    When crafting your explanations, please adhere to the following guidelines:
-      - Prioritize medical accuracy: Ensure all information provided is up-to-date and reflects current medical consensus. Use evidence-based medical knowledge to inform your responses.
-      - Clarify complex concepts: Break down medical terms and concepts into understandable language. Use analogies related to everyday experiences to help explain complex ideas when possible.
-      - Provide actionable advice: Where appropriate, offer practical and specific advice that the patient can follow to address their concerns or manage their condition, including when to consult a healthcare professional.
-      - Address concerns directly: Understand and directly respond to the patient's underlying concerns or questions, offering clear explanations and reassurance about their condition or treatment options.
-      - Promote informed decision-making: Empower the patient with the knowledge they need to make informed health decisions. Highlight key considerations and options available to them in managing their health.
-    Your response should be a blend of professional medical advice and compassionate communication, creating a dialogue that educates, reassures, and empowers the patient.
-    Strive to make your response as informative and authoritative as a consultation with a human doctor, ensuring the patient feels supported and knowledgeable about their health concerns.
-    You will answer as if you are talking to a patient directly
+you will answer only what the need and in professional way. do not add extra unnecessary information. you can however chat with the patient casually
     """
 
-    # Combine structured instructions with user prompt
-    full_prompt = f"<s>[INST] {prompt} [/INST] {instructions}</s>"
+    full_prompt = f"<s>[INST] {instructions} [/INST] {prompt}</s>"
 
     with st.chat_message("assistant"):
-        # Generate response using the pipeline with structured prompting
         result = pipeline(
             task="text-generation",
             model=model,
@@ -128,13 +121,9 @@ if prompt := st.chat_input("Ask me anything about diabetes"):
             temperature=temp_values
         )(full_prompt)
 
-        # Extract the answer from the generated text
         generated_text = result[0]['generated_text']
         response = generated_text.split("</s>")[-1].strip()
 
-        # Display the response
         st.markdown(response)
 
-    # Append assistant response to session state
     st.session_state.messages.append({"role": "assistant", "content": response})
-