Update app.py

app.py

@@ -1,64 +1,289 @@
 import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+# WHOQOL-BREF questions (Same as before)
+questions = [
+    {"id": "Q1", "text": "How would you rate your quality of life?", "domain": "Overall QOL", "scores": ["Very poor", "Poor", "Neither poor nor good", "Good", "Very good"]},
+    {"id": "Q2", "text": "How satisfied are you with your health?", "domain": "Overall Health", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q3", "text": "To what extent do you feel that physical pain prevents you from doing what you need to do?", "domain": "Physical", "scores": ["Not at all", "A little", "A moderate amount", "Very much", "An extreme amount"], "reverse": True},
+    {"id": "Q4", "text": "How much do you need any medical treatment to function in your daily life?", "domain": "Physical", "scores": ["Not at all", "A little", "A moderate amount", "Very much", "An extreme amount"], "reverse": True},
+    {"id": "Q5", "text": "How much do you enjoy life?", "domain": "Psychological", "scores": ["Not at all", "A little", "A moderate amount", "Very much", "An extreme amount"]},
+    {"id": "Q6", "text": "To what extent do you feel your life to be meaningful?", "domain": "Psychological", "scores": ["Not at all", "A little", "A moderate amount", "Very much", "An extreme amount"]},
+    {"id": "Q7", "text": "How well are you able to concentrate?", "domain": "Psychological", "scores": ["Not at all", "A little", "A moderate amount", "Very much", "Extremely"]},
+    {"id": "Q8", "text": "How safe do you feel in your daily life?", "domain": "Environment", "scores": ["Not at all", "A little", "A moderate amount", "Very much", "Extremely"]},
+    {"id": "Q9", "text": "How healthy is your physical environment?", "domain": "Environment", "scores": ["Not at all", "A little", "A moderate amount", "Very much", "Extremely"]},
+    {"id": "Q10", "text": "Do you have enough energy for everyday life?", "domain": "Physical", "scores": ["Not at all", "A little", "Moderately", "Mostly", "Completely"]},
+    {"id": "Q11", "text": "Are you able to accept your bodily appearance?", "domain": "Psychological", "scores": ["Not at all", "A little", "Moderately", "Mostly", "Completely"]},
+    {"id": "Q12", "text": "Have you enough money to meet your needs?", "domain": "Environment", "scores": ["Not at all", "A little", "Moderately", "Mostly", "Completely"]},
+    {"id": "Q13", "text": "How available to you is the information that you need in your day-to-day life?", "domain": "Environment", "scores": ["Not at all", "A little", "Moderately", "Mostly", "Completely"]},
+    {"id": "Q14", "text": "To what extent do you have the opportunity for leisure activities?", "domain": "Environment", "scores": ["Not at all", "A little", "Moderately", "Mostly", "Completely"]},
+    {"id": "Q15", "text": "How well are you able to get around?", "domain": "Physical", "scores": ["Very poor", "Poor", "Neither poor nor good", "Good", "Very good"]},
+    {"id": "Q16", "text": "How satisfied are you with your sleep?", "domain": "Physical", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q17", "text": "How satisfied are you with your ability to perform your daily living activities?", "domain": "Physical", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q18", "text": "How satisfied are you with your capacity for work?", "domain": "Physical", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q19", "text": "How satisfied are you with yourself?", "domain": "Psychological", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q20", "text": "How satisfied are you with your personal relationships?", "domain": "Social", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q21", "text": "How satisfied are you with your sex life?", "domain": "Social", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q22", "text": "How satisfied are you with the support you get from your friends?", "domain": "Social", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q23", "text": "How satisfied are you with the conditions of your living place?", "domain": "Environment", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q24", "text": "How satisfied are you with your access to health services?", "domain": "Environment", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q25", "text": "How satisfied are you with your transport?", "domain": "Environment", "scores": ["Very dissatisfied", "Dissatisfied", "Neither satisfied nor dissatisfied", "Satisfied", "Very satisfied"]},
+    {"id": "Q26", "text": "How often do you have negative feelings such as blue mood, despair, anxiety, depression?", "domain": "Psychological", "scores": ["Never", "Seldom", "Quite often", "Very often", "Always"], "reverse": True}
+]
+
+# Domain compositions and transformation formula (Same as before)
+domain_info = {
+    "Physical": {
+        "questions": ["Q3", "Q4", "Q10", "Q15", "Q16", "Q17", "Q18"],
+        "transform": lambda raw: (raw - 7) * (100/28)
+    },
+    "Psychological": {
+        "questions": ["Q5", "Q6", "Q7", "Q11", "Q19", "Q26"],
+        "transform": lambda raw: (raw - 6) * (100/24)
+    },
+    "Social": {
+        "questions": ["Q20", "Q21", "Q22"],
+        "transform": lambda raw: (raw - 3) * (100/12)
+    },
+    "Environment": {
+        "questions": ["Q8", "Q9", "Q12", "Q13", "Q14", "Q23", "Q24", "Q25"],
+        "transform": lambda raw: (raw - 8) * (100/32)
+    },
+    "Overall QOL": {
+        "questions": ["Q1"],
+        "transform": lambda raw: (raw - 1) * (100/4)
+    },
+    "Overall Health": {
+        "questions": ["Q2"],
+        "transform": lambda raw: (raw - 1) * (100/4)
+    }
+}
+
+# Map domain names to full names for display (Same as before)
+domain_full_names = {
+    "Physical": "Physical Health",
+    "Psychological": "Psychological Well-being",
+    "Social": "Social Relationships",
+    "Environment": "Environment",
+    "Overall QOL": "Overall Quality of Life",
+    "Overall Health": "Overall Health Satisfaction"
+}
+
+# Define interpretation ranges and interventions (Same as before)
+interpretation_ranges = {
+    "Physical Health": {
+        "high_range": 70,
+        "medium_range": (50, 69),
+        "low_range": 49,
+        "high_inference": "Good physical well-being, minimal limitations, good energy and mobility.",
+        "medium_inference": "Mild to moderate physical limitations, some pain, or reduced energy.",
+        "low_inference": "Significant physical health limitations, pain, fatigue, poor mobility.",
+        "high_intervention": "Maintain a healthy lifestyle, regular exercise, balanced nutrition.",
+        "medium_intervention": "Increase physical activity, improve sleep, manage minor pain.",
+        "low_intervention": "Medical intervention, physical therapy, chronic disease management."
+    },
+    "Psychological Health": {
+        "high_range": 75,
+        "medium_range": (50, 74),
+        "low_range": 49,
+        "high_inference": "Strong emotional resilience, positive self-esteem, low anxiety or stress.",
+        "medium_inference": "Moderate psychological health, occasional stress, some mood fluctuations.",
+        "low_inference": "High emotional distress, anxiety, depression, low self-worth.",
+        "high_intervention": "Continue positive mental health practices, social engagement.",
+        "medium_intervention": "Use stress reduction techniques, engage in self-care routines.",
+        "low_intervention": "Psychological counseling, cognitive behavioral therapy, medication if needed."
+    },
+    "Social Relationships": {
+        "high_range": 70,
+        "medium_range": (50, 69),
+        "low_range": 49,
+        "high_inference": "Strong social connections, good interpersonal support, high satisfaction.",
+        "medium_inference": "Moderate social interactions, may experience loneliness or minor conflicts.",
+        "low_inference": "Weak social relationships, lack of support, loneliness, or isolation.",
+        "high_intervention": "Sustain social connections, participate in group/community activities.",
+        "medium_intervention": "Strengthen personal relationships, seek social support groups.",
+        "low_intervention": "Social integration programs, mental health support, relationship therapy."
+    },
+    "Environment": {
+        "high_range": 70,
+        "medium_range": (50, 69),
+        "low_range": 49,
+        "high_inference": "Safe and stable environment, access to resources and healthcare, financial security.",
+        "medium_inference": "Some dissatisfaction with living conditions, financial constraints, or safety concerns.",
+        "low_inference": "Unstable living conditions, financial stress, poor healthcare access, safety concerns.",
+        "high_intervention": "Maintain financial stability, ensure continuous access to healthcare.",
+        "medium_intervention": "Identify key environmental issues, seek financial or housing support.",
+        "low_intervention": "Government or NGO assistance for financial/housing needs, healthcare access."
+    }
+}
+
+# Initialize the InferenceClient
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")  # Replace with a different model if desired
+
+
+# Function to get interpretation from the LLM
+def get_llm_interpretation(domain, score, interpretation_ranges):
+    if domain not in interpretation_ranges:
+        return "No interpretation available."
+
+    range_info = interpretation_ranges[domain]
+
+    if score >= range_info["high_range"]:
+        inference = range_info["high_inference"]
+        intervention = range_info["high_intervention"]
+        level = "High"
+    elif range_info["medium_range"][0] <= score <= range_info["medium_range"][1]:
+        inference = range_info["medium_inference"]
+        intervention = range_info["medium_intervention"]
+        level = "Medium"
+    else:
+        inference = range_info["low_inference"]
+        intervention = range_info["low_intervention"]
+        level = "Low"
+
+    prompt = f"""
+    Based on the WHOQOL-BREF assessment results for {domain}, the score is {score}, which is categorized as {level}.
+    The inference is: {inference}
+    Suggested intervention: {intervention}
+
+    Provide a more detailed and personalized interpretation, including potential causes and specific recommendations.
+
+    Response should be concise, empathetic, and helpful.
+    """
+
+    try:
+        # Call the InferenceClient
+        interpretation = client.text_generation(prompt, max_new_tokens=200)  # Adjust max_new_tokens as needed
+        return interpretation
+    except Exception as e:
+        return f"Error getting interpretation from LLM: {e}"
+
+
+# Function to calculate domain scores (Same as before)
+def calculate_scores(responses):
+    domain_scores = {}
+
+    for domain, info in domain_info.items():
+        domain_questions = info["questions"]
+        total_score = 0
+
+        for q_id in domain_questions:
+            q_index = next((i for i, q in enumerate(questions) if q["id"] == q_id), None)
+            if q_index is not None and responses.get(q_id) is not None:
+                if questions[q_index].get("reverse", False):
+                    total_score += 6 - responses[q_id]
+                else:
+                    total_score += responses[q_id]
+
+        transformed_score = info["transform"](total_score)
+        domain_scores[domain] = round(transformed_score, 2)
+
+    return domain_scores
+
+
+# Function to interpret the score range
+def interpret_score(domain, score):
+    if domain not in interpretation_ranges:
+        return "Unknown", "No interpretation available."
 
+    range_info = interpretation_ranges[domain]
+    high_range = range_info["high_range"]
+    medium_range = range_info["medium_range"]
+    low_range = range_info["low_range"]
+
+    if score >= high_range:
+        return (
+            "High",
+            f"{range_info['high_inference']} {range_info['high_intervention']}"
+        )
+    elif range_info["medium_range"][0] <= score <= range_info["medium_range"][1]:
+        return (
+            "Medium",
+            f"{range_info['medium_inference']} {range_info['medium_intervention']}"
+        )
+    else:
+        return (
+            "Low",
+            f"{range_info['low_inference']} {range_info['low_intervention']}"
+        )
+
+# Define the Gradio interface
+def whoqol_assessment(*responses):  # *args to take variable number of responses
+
+    # Map responses to question IDs
+    responses_dict = {q["id"]: int(r) for q, r in zip(questions, responses)} # responses from gradio are strings, need convert to int
+
+    # Calculate scores
+    scores = calculate_scores(responses_dict)
+
+    if scores is None:
+        return "Please answer all questions."  # Error message if not all questions are answered
+
+    # Prepare data for radar chart
+    main_domains = ["Physical", "Psychological", "Social", "Environment"]
+    main_scores = [scores[domain] for domain in main_domains]
+
+    # Create radar chart (using Matplotlib)
+    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
+
+    # Angle for each domain
+    angles = np.linspace(0, 2 * np.pi, len(main_domains), endpoint=False).tolist()
+    angles += angles[:1]  # Close the loop
+
+    # Add scores (with loop closure)
+    values = main_scores + [main_scores[0]]
+
+    # Plot and fill the radar chart
+    ax.plot(angles, values, 'o-', linewidth=2, color='blue')
+    ax.fill(angles, values, color='blue', alpha=0.25)
+
+    # Add labels
+    domain_labels = [domain_full_names[domain] for domain in main_domains]
+    ax.set_xticks(angles[:-1])
+    ax.set_xticklabels(domain_labels, fontsize=10)
+
+    # Set y-axis limits
+    ax.set_ylim(0, 100)
+    ax.set_yticks([0, 25, 50, 75, 100])
+    ax.set_yticklabels(['0', '25', '50', '75', '100'], fontsize=8)  # Smaller font
+
+    # Add grid lines
+    ax.grid(True, linestyle='-', alpha=0.7)
+
+    # Add title
+    ax.set_title('WHOQOL-BREF Domain Scores', size=12, y=1.1) # Smaller Title
+
+    # Detailed Interpretations and store them in text
+    interpretations_text = ""
+    for domain in ["Physical", "Psychological", "Social", "Environment", "Overall QOL", "Overall Health"]:
+        score = scores[domain]
+        llm_interpretation = get_llm_interpretation(domain_full_names[domain], score, interpretation_ranges)
+        interpretations_text += f"**{domain_full_names[domain]}**: {llm_interpretation}\n\n"
+
+    return fig, interpretations_text
+
+
+# Create Gradio inputs
+inputs = []
+for q in questions:
+    inputs.append(gr.Radio(choices=q["scores"], label=q["text"]))  # Use Radio instead of Checkbox
+
+# Create Gradio outputs
+radar_chart_output = gr.Plot()
+interpretations_output = gr.Markdown() # markdown, because I used "**" in text. This format allow to view text in bold format.
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=whoqol_assessment,
+    inputs=inputs,
+    outputs=[radar_chart_output, interpretations_output],
+    title="WHOQOL-BREF Quality of Life Assessment",
+    description="Complete the questionnaire to receive your quality of life assessment across different domains.",
+)
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio interface
+iface.launch()