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README.md

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+---
+title: Psychologist Chatbot
+emoji: 📈
+colorFrom: yellow
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.39.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: Interactive chatbot as a psychologist
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+from homepage import display_homepage
+from chatbot import display_chatbot
+from predict_stress import display_predict_stress
+from session_state import initialize_session_state
+
+initialize_session_state()
+
+# Main logic to switch between pages
+if 'page' not in st.session_state:
+    st.session_state.page = "home"
+
+if st.session_state.page == "home":
+    display_homepage()
+elif st.session_state.page == "chat":
+    display_chatbot()
+elif st.session_state.page == "stress":
+    display_predict_stress()
+
+# Adjust the layout to reduce white space
+st.markdown("<style>div.stContainer {padding-top: 0;}</style>", unsafe_allow_html=True)

bash_profile.txt

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+export HF_TOKEN=$(cat ~/.secret/hugging-face.txt)

chatbot.py

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+import streamlit as st
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from transformers import pipeline
+from langchain_huggingface import HuggingFaceEndpoint
+import numpy as np
+from pydub import AudioSegment
+import os
+
+# Model IDs
+model_id = "unsloth/Llama-3.2-1B-Instruct"
+model2_id = "mistralai/Mistral-7B-Instruct-v0.3"
+whisper_model = "openai/whisper-small"  # Using Whisper model for audio transcription
+
+def get_llm_hf_inference(model_id, max_new_tokens=128, temperature=0.1):
+    """Returns a language model for HuggingFace inference."""
+    try:
+        llm = HuggingFaceEndpoint(
+            repo_id=model_id,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            token=os.getenv("HF_TOKEN")
+        )
+        return llm
+    except Exception as e:
+        st.error(f"Error initializing model: {e}")
+        return None
+ 
+# Initialize Whisper transcription model
+def load_transcription_model():
+    try:
+        transcriber = pipeline("automatic-speech-recognition", model=whisper_model)
+        return transcriber
+    except Exception as e:
+        st.error(f"Error loading Whisper model: {e}")
+        return None
+ 
+# Preprocess audio to 16kHz mono
+def preprocess_audio(file):
+    audio = AudioSegment.from_file(file).set_frame_rate(16000).set_channels(1)
+    audio_samples = np.array(audio.get_array_of_samples()).astype(np.float32) / (2**15)
+    return audio_samples
+ 
+# Function to transcribe audio with preprocessing
+def transcribe_audio(file, transcriber):
+    audio = preprocess_audio(file)
+    transcription = transcriber(audio)["text"]
+    return transcription
+
+# Chatbot page content
+def display_chatbot():
+    st.title("Personal Psychologist Chatbot")
+    st.markdown(f"*This is a simple chatbot that acts as a psychologist and gives solutions to your psychological problems. It uses the {model_id}.*")
+ 
+    # Sidebar for settings
+    with st.sidebar:
+        # Reset Chat History
+        reset_history = st.button("Reset Chat History")
+        go_home = st.button("Back to Home")
+        if go_home:
+            st.session_state.page = "home"
+            st.experimental_rerun()  # Go back to homepage
+ 
+    # Initialize or reset chat history
+    if reset_history:
+        st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}]
+ 
+    def get_response(system_message, chat_history, user_text, model_id, max_new_tokens=256):
+        """Generates a response from the chatbot model."""
+        hf = get_llm_hf_inference(model_id=model_id, max_new_tokens=max_new_tokens)
+        if hf is None:
+            return "Error: Model not initialized.", chat_history
+ 
+        # Create the prompt template
+        prompt = PromptTemplate.from_template(
+            (
+                "[INST] {system_message}"
+                "\nCurrent Conversation:\n{chat_history}\n\n"
+                "\nUser: {user_text}.\n [/INST]"
+                "\nAI:"
+            )
+        )
+        chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
+ 
+        # Generate the response
+        response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
+        response = response.split("AI:")[-1].strip()
+ 
+        # Check for "thank you" in the user's input and prompt for report
+        if "thank you" in user_text.lower():
+            follow_up_question = "Would you like to have a report of your current health?"
+            response += f"\n\n{follow_up_question}"
+ 
+        # Update the chat history
+        chat_history.append({'role': 'user', 'content': user_text})
+        chat_history.append({'role': 'assistant', 'content': response})
+        return response, chat_history
+ 
+    def get_summary_of_chat_history(chat_history, model2_id):
+        """Generates a summary of the entire chat history using GPT-2."""
+        hf = get_llm_hf_inference(model_id=model2_id, max_new_tokens=256)
+        if hf is None:
+            return "Error: Model not initialized."
+ 
+        # Create the summary prompt
+        chat_content = "\n".join([f"{message['role']}: {message['content']}" for message in chat_history])
+        prompt = PromptTemplate.from_template(
+            f"Please summarize the following conversation and generate a report of the user's current health:\n\n{chat_content}"
+        )
+ 
+        summary = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
+        summary_response = summary.invoke(input={})
+ 
+        return summary_response
+ 
+    # Load Whisper model for transcription
+    transcriber = load_transcription_model()
+ 
+    # User input for audio and text
+    st.markdown("### Choose your input:")
+    audio_file = st.file_uploader("Upload an audio file for transcription", type=["mp3", "wav", "m4a"])
+    st.session_state.user_text = st.chat_input(placeholder="Or enter your text here.")
+ 
+    # Check if audio file is uploaded and transcribe if available
+    if audio_file is not None and transcriber:
+        with st.spinner("Transcribing audio..."):
+            try:
+                st.session_state.user_text = transcribe_audio(audio_file, transcriber)
+                st.success("Audio transcribed successfully!")
+            except Exception as e:
+                st.error(f"Error transcribing audio: {e}")
+ 
+    # Chat interface
+    output_container = st.container()
+ 
+    # Display chat messages
+    with output_container:
+        for message in st.session_state.chat_history:
+            if message['role'] == 'system':
+                continue
+            with st.chat_message(message['role'], avatar=st.session_state['avatars'][message['role']]):
+                st.markdown(message['content'])
+ 
+    # Process text input for chatbot response
+    if st.session_state.user_text:
+        with st.chat_message("user", avatar=st.session_state.avatars['user']):
+            st.markdown(st.session_state.user_text)
+ 
+        with st.chat_message("assistant", avatar=st.session_state.avatars['assistant']):
+            with st.spinner("Addressing your concerns..."):
+                response, st.session_state.chat_history = get_response(
+                    system_message=st.session_state.system_message,
+                    user_text=st.session_state.user_text,
+                    chat_history=st.session_state.chat_history,
+                    model_id=model_id,
+                    max_new_tokens=st.session_state.max_response_length,
+                )
+                st.markdown(response)
+ 
+                # Check if the user has agreed to the report
+                if "yes" in st.session_state.user_text.lower() and "Would you like to have a report of your current health?" in response:
+                    with st.spinner("Generating your health report..."):
+                        report = get_summary_of_chat_history(st.session_state.chat_history, model2_id)
+                        st.markdown(report)

homepage.py

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+import streamlit as st
+
+# Homepage content
+def display_homepage():
+    st.markdown(
+        """
+    <div style="text-align: center; margin-top: 50px; font-size: 60px; font-weight: bold; color: #2c3e50; margin-left: auto; margin-right: auto;">
+        Your personal AI Therapist
+    </div>
+        """, unsafe_allow_html=True
+    )
+ 
+    st.markdown(
+        """
+        <div style="text-align: center; font-size: 20px; color: #000000; margin-top: 20px; max-width: 700px; margin-left: auto; margin-right: auto;">
+            This is your healthcare chatbot that streamlines outpatient care, solves routine queries 24/7, and effortlessly automates appointment bookings, prescriptions, and reports. Let AI help you with your mental health journey.
+        </div>
+        """, unsafe_allow_html=True
+    )
+ 
+    st.markdown(
+        """
+        <style>
+        .stApp {
+            background-image: url('https://images.pexels.com/photos/2680270/pexels-photo-2680270.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1');
+            background-size: cover;
+            background-position: center;
+            background-repeat: no-repeat;
+            height: 100vh;
+            color: white;
+        }
+ 
+        .stButton>button {
+            background-color: #2980b9;
+            color: white;
+            font-size: 18px;
+            font-weight: bold;
+            padding: 15px 30px;
+            border-radius: 8px;
+            border: none;
+            box-shadow: 0px 5px 15px rgba(0, 0, 0, 0.1);
+            cursor: pointer;
+            transition: background-color 0.3s ease;
+        }
+ 
+        .stButton>button:hover {
+            background-color: #3498db;
+        }
+        </style>
+        """, unsafe_allow_html=True
+    )
+ 
+    col1, col2 = st.columns([1, 1])
+ 
+    with col1:
+        if st.button("Start Chat", key="start_chat_button"):
+            st.session_state.page = "chat"
+            st.experimental_rerun()  # Trigger page change
+ 
+    with col2:
+        if st.button("Predict Stress", key="predict_stress_button"):
+            st.session_state.page = "stress"
+            st.experimental_rerun()  # Trigger page change

predict_stress.py

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+import streamlit as st
+import xgboost as xgb
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+xgboostmodel_id = "Sannidhi/stress_prediction_xgboost_model"
+xgboost_model = None
+ 
+def load_xgboost_model():
+    global xgboost_model
+    try:
+        # Download the model from Hugging Face using huggingface_hub
+        model_path = hf_hub_download(repo_id="Sannidhi/stress_prediction_xgboost_model", filename="xgboost_model.json")
+       
+        # Load the model into XGBoost
+        xgboost_model = xgb.Booster()
+        xgboost_model.load_model(model_path)  # Load the model into the Booster object
+       
+        return True
+    except Exception as e:
+        st.error(f"Error loading XGBoost model from Hugging Face: {e}")
+        return False
+ 
+def display_predict_stress():
+    st.title("Predict Stress Level")
+    st.markdown("Answer the questions below to predict your stress level.")
+ 
+    # Sidebar for navigation
+    with st.sidebar:
+        go_home = st.button("Back to Home")
+        if go_home:
+            st.session_state.page = "home"
+            st.experimental_rerun()  # Go back to homepage
+    
+    load_xgboost_model()
+ 
+    # Define the form with dropdowns for user input
+    with st.form(key="stress_form"):
+        # Define the questions and their options
+        stress_questions = {
+            "How many fruits or vegetables do you eat every day?": ["0", "1", "2", "3", "4", "5"],
+            "How many new places do you visit in an year?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "How many people are very close to you?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "How many people do you help achieve a better life?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "With how many people do you interact with during a typical day?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "How many remarkable achievements are you proud of?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "How many times do you donate your time or money to good causes?": ["0", "1", "2", "3", "4", "5"],
+            "How well do you complete your weekly to-do lists?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "In a typical day, how many hours do you experience 'FLOW'? (Flow is defined as the mental state, in which you are fully immersed in performing an activity. You then experience a feeling of energized focus, full involvement, and enjoyment in the process of this activity)": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "How many steps (in thousands) do you typically walk everyday?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "For how many years ahead is your life vision very clear for?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "About how long do you typically sleep?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "How many days of vacation do you typically lose every year?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "How often do you shout or sulk at somebody?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "How sufficient is your income to cover basic life expenses (1 for insufficient, 2 for sufficient)?": ["1", "2"],
+            "How many recognitions have you received in your life?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "How many hours do you spend everyday doing what you are passionate about?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "In a typical week, how many times do you have the opportunity to think about yourself?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+            "Age (1 = 'Less than 20' 2 = '21 to 35' 3 = '36 to 50' 4 = '51 or more')": ["1", "2", "3", "4"],
+            "Gender (1 = 'Female', 0 = 'Male')": ["0", "1"]
+        }
+
+        # Map the question strings to model feature names
+        question_to_feature_map = {
+            "How many fruits or vegetables do you eat every day?": "FRUITS_VEGGIES",
+            "How many new places do you visit in an year?": "PLACES_VISITED",
+            "How many people are very close to you?": "CORE_CIRCLE",
+            "How many people do you help achieve a better life?": "SUPPORTING_OTHERS",
+            "With how many people do you interact with during a typical day?": "SOCIAL_NETWORK",
+            "How many remarkable achievements are you proud of?": "ACHIEVEMENT",
+            "How many times do you donate your time or money to good causes?": "DONATION",
+            "How well do you complete your weekly to-do lists?": "TODO_COMPLETED",
+            "In a typical day, how many hours do you experience 'FLOW'? (Flow is defined as the mental state, in which you are fully immersed in performing an activity. You then experience a feeling of energized focus, full involvement, and enjoyment in the process of this activity)": "FLOW",
+            "How many steps (in thousands) do you typically walk everyday?": "DAILY_STEPS",
+            "For how many years ahead is your life vision very clear for?": "LIVE_VISION",
+            "About how long do you typically sleep?": "SLEEP_HOURS",
+            "How many days of vacation do you typically lose every year?": "LOST_VACATION",
+            "How often do you shout or sulk at somebody?": "DAILY_SHOUTING",
+            "How sufficient is your income to cover basic life expenses (1 for insufficient, 2 for sufficient)?": "SUFFICIENT_INCOME",
+            "How many recognitions have you received in your life?": "PERSONAL_AWARDS",
+            "How many hours do you spend everyday doing what you are passionate about?": "TIME_FOR_PASSION",
+            "In a typical week, how many times do you have the opportunity to think about yourself?": "WEEKLY_MEDITATION",
+            "Age (1 = 'Less than 20' 2 = '21 to 35' 3 = '36 to 50' 4 = '51 or more')": "AGE",
+            "Gender (1 = 'Female', 0 = 'Male')": "GENDER"
+        }
+
+        # Map the responses to numerical values
+        response_map = {str(i): i for i in range(11)}  # Mapping 0-10 to 0-10
+        response_map.update({"1": 1, "2": 2})  # Mapping "1" and "2" for certain questions
+
+        # Store user responses
+        responses = {}
+        for question, options in stress_questions.items():
+            responses[question] = st.selectbox(question, options)
+
+        # Submit button
+        submit_button = st.form_submit_button("Submit")
+
+        # When submit is clicked, process the responses and make a prediction
+        if submit_button:
+            # Convert responses to feature dictionary based on the feature names
+            feature_dict = {question_to_feature_map[q]: response_map[responses[q]] for q in stress_questions.keys()}
+
+            # Convert to pandas DataFrame
+            feature_df = pd.DataFrame([feature_dict])
+
+            # Make prediction
+            try:
+                dmatrix = xgb.DMatrix(feature_df)
+                prediction = xgboost_model.predict(dmatrix)
+                st.markdown(f"### Predicted Stress Level: {prediction[0]:.2f}")
+                st.markdown("Higher values indicate higher stress levels.")
+            except Exception as e:
+                st.error(f"Error making prediction: {e}")

requirements.txt

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+transformers
+huggingface_hub
+streamlit
+langchain_core
+langchain_community
+langchain_huggingface
+langchain_text_splitters
+accelerate
+watchdog
+tqdm
+pydub
+bitsandbytes
+git+https://github.com/openai/whisper.git 
+torch
+torchaudio
+torchvision
+streamlit-mic-recorder
+ffmpeg
+openai-whisper
+torchaudio

session_state.py

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+# session_state.py
+
+import streamlit as st
+
+def initialize_session_state():
+    if "avatars" not in st.session_state:
+        st.session_state.avatars = {'user': None, 'assistant': None}
+
+    if 'user_text' not in st.session_state:
+        st.session_state.user_text = None
+
+    if "max_response_length" not in st.session_state:
+        st.session_state.max_response_length = 1000
+
+    if "system_message" not in st.session_state:
+        st.session_state.system_message = "friendly AI conversing with a human user"
+
+    if "starter_message" not in st.session_state:
+        st.session_state.starter_message = "Hello, there! How can I help you today?"
+
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}]