| import gradio as gr
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| import numpy as np
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| import openai
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| import os
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| import zipfile
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| from stable_baselines3 import PPO
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| import matplotlib.pyplot as plt
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| import io
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| import base64
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|
|
|
|
| openai.api_key = os.getenv("OPENAI_API_KEY")
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|
|
|
|
| if not os.path.exists("ppo_mental_health_model_expanded"):
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| with zipfile.ZipFile("ppo_mental_health_model_expanded.zip", "r") as zip_ref:
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| zip_ref.extractall(".")
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|
|
|
|
| ppo_model = PPO.load("ppo_mental_health_model_expanded")
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|
|
|
|
| ACTIONS = ['meditation', 'talk_therapy', 'journal_prompt', 'breathing_ex', 'video', 'nature_walk', 'soothing_music', 'gratitude_exercise']
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| INDEX_TO_ACTION = {i: a for i, a in enumerate(ACTIONS)}
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| suggestion_map = {
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| 'meditation': "Would you like to try a short meditation exercise? 🧘",
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| 'talk_therapy': "Talking to someone might help. Want to explore talk therapy?",
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| 'journal_prompt': "Journaling might help you express your thoughts. 📝",
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| 'breathing_ex': "Try a calming breathing exercise. Inhale deeply... 💨",
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| 'video': "Here’s a calming video that might lift your mood. 🎥",
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| 'nature_walk': "How about a short walk in nature? A little fresh air might help. 🌿",
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| 'soothing_music': "Listening to soothing music might help you relax. 🎶",
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| 'gratitude_exercise': "Let's try a quick gratitude exercise. Name one thing you're thankful for. 🙏"
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| }
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|
|
|
|
| mood_score_map = {
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| 'positive': 0.8, 'negative': 0.3,
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| 'joy': 0.9, 'love': 0.8, 'surprise': 0.6, 'neutral': 0.5,
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| 'sadness': 0.3, 'fear': 0.2, 'anger': 0.2, 'anxiety': 0.3,
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| 'disgust': 0.2, 'guilt': 0.2, 'hope': 0.7, 'calm': 0.75
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| }
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|
|
|
|
| chat_history = []
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| mood_scores = []
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| suggestion_memory = {action: 0 for action in ACTIONS}
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|
|
|
|
| def get_mood_from_openai(user_input, history):
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| chat_context = "\n".join([f"User: {msg}" for msg in history[-3:]])
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| prompt = f"""You are a mental health assistant. Analyze the user's emotional state from their recent conversation and return one mood word from:
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| ["joy", "sadness", "anger", "fear", "anxiety", "guilt", "neutral", "love", "surprise", "disgust", "hope", "calm", "positive", "negative"].
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| Conversation:
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| {chat_context}
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| User: {user_input}
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| Mood:"""
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| response = openai.chat.completions.create(
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| model="gpt-3.5-turbo",
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| messages=[{"role": "user", "content": prompt}],
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| temperature=0.2,
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| max_tokens=5,
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| timeout=20
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| )
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| return response.choices[0].message.content.strip().lower()
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|
|
|
|
| def plot_mood_trend():
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| fig, ax = plt.subplots()
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| ax.plot(range(1, len(mood_scores)+1), mood_scores, marker='o')
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| ax.set_title("Mood Score Over Time")
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| ax.set_xlabel("Turn")
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| ax.set_ylabel("Mood Score")
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| ax.set_ylim(0, 1)
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| filepath = "mood_trend.png"
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| plt.savefig(filepath)
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| plt.close(fig)
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| return filepath
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|
|
|
|
| def chatbot_response(user_input, history=None):
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| try:
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| chat_history.append(user_input)
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| mood_label = get_mood_from_openai(user_input, chat_history)
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| mood_score = mood_score_map.get(mood_label, 0.4)
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| mood_scores.append(mood_score)
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|
|
| obs = np.array([mood_score], dtype=np.float32).reshape(1, -1)
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| action, _ = ppo_model.predict(obs)
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| suggestion = INDEX_TO_ACTION[int(action)]
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| suggestion_memory[suggestion] += 1
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| response = suggestion_map[suggestion]
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|
|
| chat_output = f"🤖 I sense you might be feeling *{mood_label}*. {response}"
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| return chat_output, plot_mood_trend()
|
|
|
| except Exception as e:
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| import traceback
|
| traceback.print_exc()
|
| return f"❌ Error: {str(e)}", None
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|
|
|
|
| gr.Interface(
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| fn=chatbot_response,
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| inputs=gr.Textbox(lines=2, placeholder="How are you feeling today?"),
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| outputs=[gr.Textbox(label="Chatbot Response"), gr.Image(label="Mood Trend")],
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| title="Mental Health Support Bot",
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| description="This chatbot tracks mood over time, remembers your context, and suggests helpful wellness strategies."
|
| ).launch()
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|
|
