Create app.py

app.py

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+import streamlit as st
+from streamlit_option_menu import option_menu
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+
+# Set page configuration
+st.set_page_config(
+    page_title="VitalCare GPT",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Load Hugging Face models and tokenizers
+@st.cache_resource
+def load_models():
+    pipe_disease = pipeline("text-generation", model="harishussain12/PastelMed")
+    tokenizer_lynxmed = AutoTokenizer.from_pretrained("harishussain12/LynxMed")
+    model_lynxmed = AutoModelForCausalLM.from_pretrained("harishussain12/LynxMed")
+
+    tokenizer_neuramed = AutoTokenizer.from_pretrained("harishussain12/NeuraMed")
+    model_neuramed = AutoModelForCausalLM.from_pretrained("harishussain12/NeuraMed")
+
+    tokenizer_skyemed = AutoTokenizer.from_pretrained("harishussain12/SkyeMed")
+    model_skyemed = AutoModelForCausalLM.from_pretrained("harishussain12/SkyeMed")
+
+    tokenizer_clixmed = AutoTokenizer.from_pretrained("harishussain12/ClixMed")
+    model_clixmed = AutoModelForCausalLM.from_pretrained("harishussain12/ClixMed")
+
+    return pipe_disease, (tokenizer_lynxmed, model_lynxmed), (tokenizer_neuramed, model_neuramed), (tokenizer_skyemed, model_skyemed), (tokenizer_clixmed, model_clixmed)
+
+# Function to create pipelines for all models
+@st.cache_resource
+def create_pipelines():
+    pipe_disease, (tokenizer_lynxmed, model_lynxmed), (tokenizer_neuramed, model_neuramed), (tokenizer_skyemed, model_skyemed), (tokenizer_clixmed, model_clixmed) = load_models()
+    pipeline_lynxmed = pipeline("text-generation", model=model_lynxmed, tokenizer=tokenizer_lynxmed)
+    pipe_neuramed = pipeline("text-generation", model=model_neuramed, tokenizer=tokenizer_neuramed)
+    pipe_skyemed = pipeline("text-generation", model=model_skyemed, tokenizer=tokenizer_skyemed)
+    pipeline_clixmed = pipeline("text-generation", model=model_clixmed, tokenizer=tokenizer_clixmed)
+
+    return {
+        "PastelMed": pipe_disease,
+        "LynxMed": pipeline_lynxmed,
+        "NeuraMed": pipe_neuramed,
+        "SkyeMed": pipe_skyemed,
+        "ClixMed": pipeline_clixmed
+    }
+
+# Load pipelines
+pipelines = create_pipelines()
+
+# Sidebar with navigation
+with st.sidebar:
+    selected = option_menu(
+        menu_title=None,  # Remove the Navigation title
+        options=["Home", "Spaces", "About"],
+        icons=["house", "search", "info-circle"],
+        menu_icon="cast",
+        default_index=0,
+        styles={
+            "container": {"padding": "0!important", "background-color": "#3e4a5b"},
+            "icon": {"color": "#ffffff", "font-size": "16px"},
+            "nav-link": {
+                "font-size": "15px",
+                "text-align": "left",
+                "margin": "0px",
+                "color": "#ffffff",
+                "font-weight": "bold",
+                "padding": "10px 20px",
+            },
+            "nav-link-selected": {"background-color": "#0b2545", "color": "white"},
+        }
+    )
+
+# Initialize session state for chat history
+if 'home_chat_history' not in st.session_state:
+    st.session_state['home_chat_history'] = []
+
+if 'chat_history' not in st.session_state:
+    st.session_state['chat_history'] = {}
+
+# Define role-specific keywords or categories
+role_keywords = {
+    "Doctor": ["symptoms", "diagnosis", "treatment", "disease", "medical condition", 
+               "prescription", "procedure", "surgery", "consultation", "therapy", 
+               "prognosis", "clinical", "specialist", "check-up", "imaging", 
+               "laboratory tests", "pathology", "epidemiology", "anatomy", "physiology"],
+    "Nutritionist": ["diet", "nutrition", "meal plan", "calories", "weight management", 
+                     "vitamins", "minerals", "protein", "carbohydrates", "fats", 
+                     "healthy eating", "lifestyle", "food", "allergies", "deficiencies", 
+                     "hydration", "superfoods", "balanced diet", "supplements", "recipes"],
+    "Pharmacist": ["medication", "dosage", "side effects", "drug", "pharmacy", 
+                   "prescription", "over-the-counter", "interaction", "refill", 
+                   "formulation", "pharmacology", "pharmaceutical", "compounding", 
+                   "instructions", "contraindications", "storage", "expiry", 
+                   "dispense", "pharmacist advice", "generic drugs", "medicine"]
+}
+
+# Role prompts
+role_prompts = {
+    "Doctor": """
+        You are assisting as a doctor.
+        Tasks:
+        - Answer medical questions concisely and accurately.
+        - Respond with: "I don't know about it" if the query is not related to the medical field.
+    """,
+    "Nutritionist": """
+        You are assisting as a nutritionist.
+        Tasks:
+        - Provide dietary advice based on queries.
+        - Suggest meal plans, calorie intake, and balanced diets.
+        - Respond with: "I don't know about it" if the query is not related to nutrition.
+    """,
+    "Pharmacist": """
+        You act as a pharmacist.
+        Tasks:
+        - Provide details on medications, dosages, and side effects.
+        - Respond with: "I don't know about it" if unrelated to medicine.
+    """
+}
+
+# Function to check if query matches the role
+def is_query_relevant(role, query):
+    keywords = role_keywords.get(role, [])
+    query_lower = query.lower()
+    return any(keyword in query_lower for keyword in keywords)
+
+# Main content based on navigation
+if selected == "Home":
+    col1, col2, col3 = st.columns([1, 2, 1])
+
+    with col2:
+        st.markdown("<h1 style='text-align: center;'>VitalCare GPT</h1>", unsafe_allow_html=True)
+        st.markdown("<h3 style='text-align: center;'>How can I assist with your medical queries today?</h3>", unsafe_allow_html=True)
+
+        # Display chat history for Home section above input
+        for message in st.session_state['home_chat_history']:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+        # Model selection
+        model_selection = st.selectbox(
+            "Select a model",
+            options=["SkyeMed", "NeuraMed", "PastelMed", "LynxMed", "ClixMed"],
+            index=0
+        )
+
+        # Search box
+        search_input = st.text_input(
+            "",
+            placeholder="Type your medical question here...",
+            label_visibility="collapsed",
+            help="Ask anything related to medical knowledge."
+        )
+
+        if search_input:
+            with st.spinner("Generating response..."):
+                try:
+                    query_input = search_input
+                    response = pipelines[model_selection](query_input, max_length=200, num_return_sequences=1)
+
+                    # Save the user and assistant messages to chat history
+                    st.session_state['home_chat_history'].append({"role": "user", "content": search_input})
+                    st.session_state['home_chat_history'].append({"role": "assistant", "content": response[0]['generated_text']})
+
+                    # Display the generated response
+                    st.markdown(f"### Response:\n{response[0]['generated_text']}")
+
+                except Exception as e:
+                    st.error(f"Error generating response: {str(e)}")
+
+elif selected == "Spaces":
+    st.markdown("<h1>Spaces</h1>", unsafe_allow_html=True)
+
+    # Layout for space buttons
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        if st.button("Doctor", key="doctor", use_container_width=True):
+            st.session_state.selected_role = "Doctor"
+    with col2:
+        if st.button("Nutritionist", key="nutritionist", use_container_width=True):
+            st.session_state.selected_role = "Nutritionist"
+    with col3:
+        if st.button("Pharmacist", key="pharmacist", use_container_width=True):
+            st.session_state.selected_role = "Pharmacist"
+
+    # Display the selected role
+    if "selected_role" in st.session_state:
+        selected_role = st.session_state.selected_role
+        st.markdown(f"<h2>Selected Space: {selected_role}</h2>", unsafe_allow_html=True)
+
+        # Initialize chat history for the selected role if not already done
+        if selected_role not in st.session_state['chat_history']:
+            st.session_state['chat_history'][selected_role] = []
+
+        # Display chat history for the selected role
+        for message in st.session_state['chat_history'][selected_role]:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+        # Add model selection dropdown
+        model_selection = st.selectbox(
+            "Select a model",
+            options=["SkyeMed", "NeuraMed", "PastelMed", "LynxMed", "ClixMed"],
+            index=0
+        )
+
+        # Align query input and button on the same line
+        query_col1, query_col2 = st.columns([4, 1])
+        with query_col1:
+            query = st.text_input(
+                f"Enter your query as a {selected_role.lower()}:",
+                placeholder="Type your question here...",
+                label_visibility="collapsed"
+            )
+        with query_col2:
+            generate_clicked = st.button("Generate Response", key="generate_button")
+
+        if generate_clicked:
+            if query.strip():
+                with st.spinner("Generating response..."):
+                    try:
+                        # Check query relevance for the selected role
+                        if not is_query_relevant(selected_role, query):
+                            response_text = f"As a {selected_role.lower()}, I cannot answer this question."
+                        else:
+                            # Generate response using the selected model
+                            role_prompt = role_prompts.get(selected_role, "")
+                            formatted_query = f"\n\nquery: {query}\n"
+                            response = pipelines[model_selection](formatted_query, max_length=200, num_return_sequences=1)
+                            response_text = response[0]['generated_text']
+
+                        # Save user and assistant messages to the selected role's chat history
+                        st.session_state['chat_history'][selected_role].append({"role": "user", "content": query})
+                        st.session_state['chat_history'][selected_role].append({"role": "assistant", "content": response_text})
+
+                        # Display the response
+                        st.markdown(f"### Response:\n{response_text}")
+
+                    except Exception as e:
+                        st.error(f"Error generating response: {str(e)}")
+            else:
+                st.warning("Please enter a query before generating a response.")
+
+elif selected == "About":
+    st.markdown("<h1>About VitalCare GPT</h1>", unsafe_allow_html=True)
+    st.markdown(
+        """
+        VitalCare GPT is an advanced AI-powered platform designed to provide reliable and accurate medical insights, enabling users to access information related to healthcare and wellness effortlessly. Powered by cutting-edge language models, VitalCare GPT specializes in various domains, including general medical advice, nutritional guidance, and pharmaceutical expertise.
+
+Whether you're looking for symptoms analysis, dietary recommendations, or medication details, our platform empowers users to interact seamlessly with AI models trained on specific medical and healthcare-related datasets. VitalCare GPT offers dedicated spaces for doctors, nutritionists, and pharmacists, ensuring tailored responses to your queries.
+        """
+    )
+
+# Footer at the bottom with centered text, and adjusted when sidebar is toggled
+st.markdown("""
+    <style>
+        .footer {
+            position: fixed;
+            bottom: 10px;
+            left: 50%;
+            transform: translateX(-50%);
+            color: white;
+            padding: 8px;  /* Reduced padding */
+            border-radius: 10px;
+            font-size: 12px;  /* Smaller font size */
+            text-align: center;
+            z-index: 1000;
+            background-color: transparent;
+        }
+
+        /* Adjust position based on sidebar */
+        .footer-container {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            position: fixed;
+            bottom: 10px;
+            left: 50%;
+            transform: translateX(-50%);
+            width: 100%;
+        }
+
+        @media screen and (max-width: 900px) {
+            .footer {
+                position: fixed;
+                left: 50%;
+                transform: translateX(-50%);
+            }
+        }
+    </style>
+
+    <div class="footer-container">
+        <div class="footer">
+            This GPT may take time to generate responses and may have lower accuracy.
+        </div>
+    </div>
+""", unsafe_allow_html=True)
+
+# Floating question mark icon with tooltip
+st.markdown("""
+    <style>
+        /* Floating Question Mark Icon */
+        .help-icon {
+            position: fixed;
+            bottom: 10px;
+            right: 10px;
+            background-color: #333;
+            color: white;
+            font-size: 14px;  /* Even smaller font size */
+            border-radius: 50%;
+            padding: 6px;  /* Smaller padding */
+            width: 30px;  /* Smaller width */
+            height: 30px;  /* Smaller height */
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            cursor: pointer;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2);
+            z-index: 1000;
+        }
+
+        /* Tooltip content when hovering */
+        .help-tooltip {
+            position: fixed;
+            bottom: 50px;
+            right: 10px;
+            background-color: rgba(0, 0, 0, 0.8);
+            color: white;
+            padding: 6px;  /* Smaller padding */
+            border-radius: 10px;
+            font-size: 12px;  /* Smaller font size */
+            display: none;
+            z-index: 1000;
+        }
+
+        .help-icon:hover + .help-tooltip,
+        .help-tooltip:hover {
+            display: block;
+        }
+    </style>
+
+    <!-- Help icon and tooltip -->
+    <div class="help-icon">?</div>
+    <div class="help-tooltip">
+        Developed by<br>
+        Rayyan & Haris
+    </div>
+""", unsafe_allow_html=True)