Update app.py

app.py

@@ -1,285 +1,284 @@
-import gradio as gr
-from transformers import pipeline
-import numpy as np
-from gradio_client import Client
-import matplotlib.pyplot as plt
-from lime.lime_text import LimeTextExplainer
-from datetime import datetime
-import sqlite3
-import logging
-
-
-DB_NAME = "mental_health.db"
-
-# --- Database Setup ---
-def create_database():
-    conn = sqlite3.connect(DB_NAME)
-    cursor = conn.cursor()
-    cursor.execute("""
-        CREATE TABLE IF NOT EXISTS mental_health_analysis (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            user_id TEXT NOT NULL
-            text TEXT NOT NULL,
-            sentiment TEXT,
-            sentiment_confidence REAL,
-            disorder TEXT,
-            disorder_confidence REAL,
-            risk_level TEXT,
-            recommendations TEXT,
-            date TEXT
-        )
-    """)
-    conn.commit()
-    conn.close()
-
-# --- Insert Results into Database ---
-def insert_into_database(user_id, text, sentiment, sentiment_confidence, disorder, disorder_confidence, risk_level, recommendations, date):
-    conn = sqlite3.connect(DB_NAME)
-    cursor = conn.cursor()
-
-    # Convert list to string for recommendations
-    recommendations_str = "; ".join(recommendations)
-
-    cursor.execute("""
-        INSERT INTO mental_health (
-            user_id, text, sentiment, sentiment_confidence, disorder, disorder_confidence, risk_level, recommendations, date
-        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
-    """, (
-        text, sentiment, sentiment_confidence, disorder,
-        disorder_confidence, risk_level, recommendations_str, date
-    ))
-
-    conn.commit()
-    conn.close()
-
-create_database()
-
-class MentalHealthChatbot:
-    def __init__(self):
-        self.sentiment_client = Client("hasanmustafa0503/sentiment-api")
-        self.disorder_client = Client("hasanmustafa0503/disoreder-api")
-
-        # Label mappings
-        self.label_mapping = {
-            "LABEL_0": "ADHD",
-            "LABEL_1": "BPD",
-            "LABEL_2": "OCD",
-            "LABEL_3": "PTSD",
-            "LABEL_4": "Anxiety",
-            "LABEL_5": "Autism",
-            "LABEL_6": "Bipolar",
-            "LABEL_7": "Depression",
-            "LABEL_8": "Eating Disorders",
-            "LABEL_9": "Health",
-            "LABEL_10": "Mental Illness",
-            "LABEL_11": "Schizophrenia",
-            "LABEL_12": "Suicide Watch"
-        }
-
-        self.sentiment_mapping = {
-            "POS": "Positive",
-            "NEG": "Negative",
-            "NEU": "Neutral"
-        }
-
-        self.exercise_recommendations = {
-            # Exercise recommendations data as defined in the original code
-        }
-
-        # Initialize the LIME explainer
-        self.explainer = LimeTextExplainer(class_names=list(self.sentiment_mapping.values()) + list(self.label_mapping.values()))
-
-    def get_sentiment(self, text):
-        results = self.sentiment_client.predict(text=text, api_name="/predict")
-    
-        if results and isinstance(results, list):
-            label = results[0]['label']
-            confidence_score = results[0]['score']*100
-            sentiment = self.sentiment_mapping.get(label, "Unknown")
-            return sentiment, confidence_score
-        return "Unknown", 0
-
-
-    def get_disorder(self, text, threshold=35):
-        results = self.disorder_client.predict(text=text, api_name="/predict")
-        if results and isinstance(results, list):  
-            best_result = results[0]
-            disorder_confidence = best_result["score"] * 100
-        
-            if disorder_confidence > threshold:
-                disorder_label = self.label_mapping.get(best_result["label"], "Unknown Disorder")
-
-                if disorder_confidence < 50:
-                    risk_level = "Low Risk"
-                elif 50 <= disorder_confidence <= 75:
-                    risk_level = "Moderate Risk"
-                else:
-                    risk_level = "High Risk"
-            
-                return disorder_label, disorder_confidence, risk_level
-
-        return "No significant disorder detected", 0.0, "No Risk"
-      
-
-    def predict_fn(self, texts):
-        sentiment_probs = []
-        disorder_probs = []
-        sentiment_labels = []
-        disorder_labels = []
-
-        for text in texts:
-            sentiment, sentiment_confidence = self.get_sentiment(text)
-            sentiment_probs.append([sentiment_confidence / 100])
-            sentiment_labels.append(sentiment)
-
-            disorder_label, disorder_confidence, risk_level = self.get_disorder(text)
-            disorder_probs.append([disorder_confidence / 100])
-            disorder_labels.append(disorder_label)
-
-        sentiment_probs = np.array(sentiment_probs)
-        disorder_probs = np.array(disorder_probs)
-
-        result = np.hstack([sentiment_probs, disorder_probs])
-
-        return result, sentiment_labels, disorder_labels
-    
-    def lime_predict_fn(self, texts):
-        result, _, _ = self.predict_fn(texts)
-        return result
-
-
-    def explain_text(self, text):
-        explanation = self.explainer.explain_instance(text, self.lime_predict_fn, num_features=5, num_samples=25)
-        explanation.as_pyplot_figure()  # Display the plot
-        plt.show()
-
-        explanation_str = "The model's prediction is influenced by the following factors: "
-        explanation_str += "; ".join([f'"{feature}" contributes with a weight of {weight:.4f}' 
-                                      for feature, weight in explanation.as_list()]) + "."
-        return explanation_str
-
-    def get_recommendations(self, condition, risk_level):
-        exercise_recommendations = {
-            "Depression": {
-            "High Risk": ["Try 10 minutes of deep breathing.", "Go for a 15-minute walk in nature.", "Practice guided meditation."],
-            "Moderate Risk": ["Write down 3 things you’re grateful for.", "Do light stretching or yoga for 10 minutes.", "Listen to calming music."],
-            "Low Risk": ["Engage in a hobby you enjoy.", "Call a friend and have a short chat.", "Do a short 5-minute mindfulness exercise."]
-            },
-            "Anxiety": {
-            "High Risk": ["Try progressive muscle relaxation.", "Use the 4-7-8 breathing technique.", "Write down your thoughts to clear your mind."],
-            "Moderate Risk": ["Listen to nature sounds or white noise.", "Take a 15-minute break from screens.", "Try a short visualization exercise."],
-            "Low Risk": ["Practice slow, deep breathing for 5 minutes.", "Drink herbal tea and relax.", "Read a book for 10 minutes."]
-            },
-            "Bipolar": {
-            "High Risk": ["Engage in grounding techniques like 5-4-3-2-1.", "Try slow-paced walking in a quiet area.", "Listen to calm instrumental music."],
-            "Moderate Risk": ["Do a 10-minute gentle yoga session.", "Keep a mood journal for self-awareness.", "Practice self-affirmations."],
-            "Low Risk": ["Engage in light exercise like jogging.", "Practice mindful eating for a meal.", "Do deep breathing exercises."]
-            },
-            "OCD": {
-            "High Risk": ["Use exposure-response prevention techniques.", "Try 5 minutes of guided meditation.", "Write down intrusive thoughts and challenge them."],
-            "Moderate Risk": ["Take a short break from triggers.", "Practice progressive relaxation.", "Engage in a calming activity like drawing."],
-            "Low Risk": ["Practice deep breathing with slow exhales.", "Listen to soft music and relax.", "Try focusing on one simple task at a time."]
-              },
-              "PTSD": {
-            "High Risk": ["Try grounding techniques (hold an object, describe it).", "Do 4-7-8 breathing for relaxation.", "Write in a trauma journal."],
-            "Moderate Risk": ["Practice mindfulness for 5 minutes.", "Engage in slow, rhythmic movement (walking, stretching).", "Listen to soothing music."],
-            "Low Risk": ["Try positive visualization techniques.", "Engage in light exercise or stretching.", "Spend time in a quiet, safe space."]
-            },
-            "Suicide Watch": {
-            "High Risk": ["Immediately reach out to a mental health professional.", "Call a trusted friend or family member.", "Try a grounding exercise like cold water on hands."],
-            "Moderate Risk": ["Write a letter to your future self.", "Listen to uplifting music.", "Practice self-care (take a bath, make tea, etc.)."],
-            "Low Risk": ["Watch a motivational video.", "Write down your emotions in a journal.", "Spend time with loved ones."]
-            },
-            "ADHD": {
-            "High Risk": ["Try structured routines for the day.", "Use a timer for focus sessions.", "Engage in short bursts of physical activity."],
-            "Moderate Risk": ["Do a quick exercise routine (jumping jacks, stretches).", "Use fidget toys to channel energy.", "Try meditation with background music."],
-            "Low Risk": ["Practice deep breathing.", "Listen to classical or instrumental music.", "Organize your workspace."]
-            },
-            "BPD": {
-            "High Risk": ["Try dialectical behavior therapy (DBT) techniques.", "Practice mindfulness.", "Use a weighted blanket for comfort."],
-            "Moderate Risk": ["Write down emotions and analyze them.", "Engage in creative activities like painting.", "Listen to calming podcasts."],
-            "Low Risk": ["Watch a lighthearted movie.", "Do breathing exercises.", "Call a friend for a short chat."]
-            },
-            "Autism": {
-            "High Risk": ["Engage in deep-pressure therapy (weighted blanket).", "Use noise-canceling headphones.", "Try sensory-friendly relaxation techniques."],
-            "Moderate Risk": ["Do repetitive physical activities like rocking.", "Practice structured breathing exercises.", "Engage in puzzles or memory games."],
-            "Low Risk": ["Spend time in a quiet space.", "Listen to soft instrumental music.", "Follow a structured schedule."]
-            },
-            "Schizophrenia": {
-            "High Risk": ["Seek immediate support from a trusted person.", "Try simple grounding exercises.", "Use distraction techniques like puzzles."],
-            "Moderate Risk": ["Engage in light physical activity.", "Listen to calming sounds or music.", "Write thoughts in a journal."],
-            "Low Risk": ["Read a familiar book.", "Do a 5-minute breathing exercise.", "Try progressive muscle relaxation."]
-            },
-            "Eating Disorders": {
-            "High Risk": ["Seek professional help immediately.", "Try self-affirmations.", "Practice intuitive eating (listen to body cues)."],
-            "Moderate Risk": ["Engage in mindful eating.", "Write down your emotions before meals.", "Do light stretching after meals."],
-            "Low Risk": ["Try a gentle walk after eating.", "Listen to calming music.", "Write a gratitude journal about your body."]
-            },
-            "Mental Health": {
-            "High Risk": ["Reach out to a mental health professional.", "Engage in deep relaxation techniques.", "Talk to a support group."],
-            "Moderate Risk": ["Write in a daily journal.", "Practice guided meditation.", "Do light physical activities like walking."],
-            "Low Risk": ["Try deep breathing exercises.", "Watch an uplifting video.", "Call a friend for a chat."]
-            }
-            
-        }
-        if condition in exercise_recommendations:
-            if risk_level in exercise_recommendations[condition]:
-                return exercise_recommendations[condition][risk_level]
-        return ["No specific recommendations available."]
-    
-
-bot = MentalHealthChatbot()
-
-def analyze_text(text):
-    if not text.strip():
-        return "No input provided.", "", "", "", "", ""
-
-    sentiment, sentiment_conf = bot.get_sentiment(text)
-    disorder, disorder_conf, risk = bot.get_disorder(text)
-
-    if risk == "High Risk":
-        logging.info("🚨 High risk detected! Alert triggered.")
-        alert_msg = "✔ 🚨 Alert Notification Triggered: High risk detected!"
-    else:
-        alert_msg = "✔ Risk is not high. No alert triggered."
-
-    recs = bot.get_recommendations(disorder, risk)
-    lime_explanation = bot.explain_text(text)
-    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-    # Insert data into DB
-    insert_into_database(
-        text,
-        sentiment,
-        sentiment_conf,
-        disorder,
-        disorder_conf,
-        risk,
-        recs,
-        timestamp
-    )
-
-    return (
-        f"{sentiment} ({sentiment_conf:.2f}%)",
-        f"{disorder} ({disorder_conf:.2f}%)",
-        risk,
-        "; ".join(recs),
-        lime_explanation,
-        alert_msg
-    )
-
-# Gradio interface
-gr.Interface(
-    fn=analyze_text,
-    inputs=gr.Textbox(lines=6, label="Describe how you're feeling..."),
-    outputs=[
-        gr.Text(label="Sentiment"),
-        gr.Text(label="Disorder"),
-        gr.Text(label="Risk Level"),
-        gr.Text(label="Recommendations"),
-        gr.Text(label="LIME Explanation"),
-        gr.Text(label="Alert Message")
-    ],
-    title="🧠 Mental Health Analysis Assistant",
-    description="This tool uses AI to detect mental health conditions based on your input and suggest possible actions."
+import gradio as gr
+from transformers import pipeline
+import numpy as np
+from gradio_client import Client
+import matplotlib.pyplot as plt
+from lime.lime_text import LimeTextExplainer
+from datetime import datetime
+import sqlite3
+import logging
+
+
+DB_NAME = "mental_health_analysis.db"
+
+# --- Database Setup ---
+def create_database():
+    conn = sqlite3.connect(DB_NAME)
+    cursor = conn.cursor()
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS mental_health_analysis (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            text TEXT NOT NULL,
+            sentiment TEXT,
+            sentiment_confidence REAL,
+            disorder TEXT,
+            disorder_confidence REAL,
+            risk_level TEXT,
+            recommendations TEXT,
+            date TEXT
+        )
+    """)
+    conn.commit()
+    conn.close()
+
+# --- Insert Results into Database ---
+def insert_into_database(text, sentiment, sentiment_confidence, disorder, disorder_confidence, risk_level, recommendations, date):
+    conn = sqlite3.connect(DB_NAME)
+    cursor = conn.cursor()
+
+    # Convert list to string for recommendations
+    recommendations_str = "; ".join(recommendations)
+
+    cursor.execute("""
+        INSERT INTO mental_health_analysis (
+            text, sentiment, sentiment_confidence, disorder, disorder_confidence, risk_level, recommendations, date
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+    """, (
+        text, sentiment, sentiment_confidence, disorder,
+        disorder_confidence, risk_level, recommendations_str, date
+    ))
+
+    conn.commit()
+    conn.close()
+
+create_database()
+
+class MentalHealthChatbot:
+    def __init__(self):
+        self.sentiment_client = Client("hasanmustafa0503/sentiment-api")
+        self.disorder_client = Client("hasanmustafa0503/disoreder-api")
+
+        # Label mappings
+        self.label_mapping = {
+            "LABEL_0": "ADHD",
+            "LABEL_1": "BPD",
+            "LABEL_2": "OCD",
+            "LABEL_3": "PTSD",
+            "LABEL_4": "Anxiety",
+            "LABEL_5": "Autism",
+            "LABEL_6": "Bipolar",
+            "LABEL_7": "Depression",
+            "LABEL_8": "Eating Disorders",
+            "LABEL_9": "Health",
+            "LABEL_10": "Mental Illness",
+            "LABEL_11": "Schizophrenia",
+            "LABEL_12": "Suicide Watch"
+        }
+
+        self.sentiment_mapping = {
+            "POS": "Positive",
+            "NEG": "Negative",
+            "NEU": "Neutral"
+        }
+
+        self.exercise_recommendations = {
+            # Exercise recommendations data as defined in the original code
+        }
+
+        # Initialize the LIME explainer
+        self.explainer = LimeTextExplainer(class_names=list(self.sentiment_mapping.values()) + list(self.label_mapping.values()))
+
+    def get_sentiment(self, text):
+        results = self.sentiment_client.predict(text=text, api_name="/predict")
+    
+        if results and isinstance(results, list):
+            label = results[0]['label']
+            confidence_score = results[0]['score']*100
+            sentiment = self.sentiment_mapping.get(label, "Unknown")
+            return sentiment, confidence_score
+        return "Unknown", 0
+
+
+    def get_disorder(self, text, threshold=35):
+        results = self.disorder_client.predict(text=text, api_name="/predict")
+        if results and isinstance(results, list):  
+            best_result = results[0]
+            disorder_confidence = best_result["score"] * 100
+        
+            if disorder_confidence > threshold:
+                disorder_label = self.label_mapping.get(best_result["label"], "Unknown Disorder")
+
+                if disorder_confidence < 50:
+                    risk_level = "Low Risk"
+                elif 50 <= disorder_confidence <= 75:
+                    risk_level = "Moderate Risk"
+                else:
+                    risk_level = "High Risk"
+            
+                return disorder_label, disorder_confidence, risk_level
+
+        return "No significant disorder detected", 0.0, "No Risk"
+      
+
+    def predict_fn(self, texts):
+        sentiment_probs = []
+        disorder_probs = []
+        sentiment_labels = []
+        disorder_labels = []
+
+        for text in texts:
+            sentiment, sentiment_confidence = self.get_sentiment(text)
+            sentiment_probs.append([sentiment_confidence / 100])
+            sentiment_labels.append(sentiment)
+
+            disorder_label, disorder_confidence, risk_level = self.get_disorder(text)
+            disorder_probs.append([disorder_confidence / 100])
+            disorder_labels.append(disorder_label)
+
+        sentiment_probs = np.array(sentiment_probs)
+        disorder_probs = np.array(disorder_probs)
+
+        result = np.hstack([sentiment_probs, disorder_probs])
+
+        return result, sentiment_labels, disorder_labels
+    
+    def lime_predict_fn(self, texts):
+        result, _, _ = self.predict_fn(texts)
+        return result
+
+
+    def explain_text(self, text):
+        explanation = self.explainer.explain_instance(text, self.lime_predict_fn, num_features=5, num_samples=25)
+        explanation.as_pyplot_figure()  # Display the plot
+        plt.show()
+
+        explanation_str = "The model's prediction is influenced by the following factors: "
+        explanation_str += "; ".join([f'"{feature}" contributes with a weight of {weight:.4f}' 
+                                      for feature, weight in explanation.as_list()]) + "."
+        return explanation_str
+
+    def get_recommendations(self, condition, risk_level):
+        exercise_recommendations = {
+            "Depression": {
+            "High Risk": ["Try 10 minutes of deep breathing.", "Go for a 15-minute walk in nature.", "Practice guided meditation."],
+            "Moderate Risk": ["Write down 3 things you’re grateful for.", "Do light stretching or yoga for 10 minutes.", "Listen to calming music."],
+            "Low Risk": ["Engage in a hobby you enjoy.", "Call a friend and have a short chat.", "Do a short 5-minute mindfulness exercise."]
+            },
+            "Anxiety": {
+            "High Risk": ["Try progressive muscle relaxation.", "Use the 4-7-8 breathing technique.", "Write down your thoughts to clear your mind."],
+            "Moderate Risk": ["Listen to nature sounds or white noise.", "Take a 15-minute break from screens.", "Try a short visualization exercise."],
+            "Low Risk": ["Practice slow, deep breathing for 5 minutes.", "Drink herbal tea and relax.", "Read a book for 10 minutes."]
+            },
+            "Bipolar": {
+            "High Risk": ["Engage in grounding techniques like 5-4-3-2-1.", "Try slow-paced walking in a quiet area.", "Listen to calm instrumental music."],
+            "Moderate Risk": ["Do a 10-minute gentle yoga session.", "Keep a mood journal for self-awareness.", "Practice self-affirmations."],
+            "Low Risk": ["Engage in light exercise like jogging.", "Practice mindful eating for a meal.", "Do deep breathing exercises."]
+            },
+            "OCD": {
+            "High Risk": ["Use exposure-response prevention techniques.", "Try 5 minutes of guided meditation.", "Write down intrusive thoughts and challenge them."],
+            "Moderate Risk": ["Take a short break from triggers.", "Practice progressive relaxation.", "Engage in a calming activity like drawing."],
+            "Low Risk": ["Practice deep breathing with slow exhales.", "Listen to soft music and relax.", "Try focusing on one simple task at a time."]
+              },
+              "PTSD": {
+            "High Risk": ["Try grounding techniques (hold an object, describe it).", "Do 4-7-8 breathing for relaxation.", "Write in a trauma journal."],
+            "Moderate Risk": ["Practice mindfulness for 5 minutes.", "Engage in slow, rhythmic movement (walking, stretching).", "Listen to soothing music."],
+            "Low Risk": ["Try positive visualization techniques.", "Engage in light exercise or stretching.", "Spend time in a quiet, safe space."]
+            },
+            "Suicide Watch": {
+            "High Risk": ["Immediately reach out to a mental health professional.", "Call a trusted friend or family member.", "Try a grounding exercise like cold water on hands."],
+            "Moderate Risk": ["Write a letter to your future self.", "Listen to uplifting music.", "Practice self-care (take a bath, make tea, etc.)."],
+            "Low Risk": ["Watch a motivational video.", "Write down your emotions in a journal.", "Spend time with loved ones."]
+            },
+            "ADHD": {
+            "High Risk": ["Try structured routines for the day.", "Use a timer for focus sessions.", "Engage in short bursts of physical activity."],
+            "Moderate Risk": ["Do a quick exercise routine (jumping jacks, stretches).", "Use fidget toys to channel energy.", "Try meditation with background music."],
+            "Low Risk": ["Practice deep breathing.", "Listen to classical or instrumental music.", "Organize your workspace."]
+            },
+            "BPD": {
+            "High Risk": ["Try dialectical behavior therapy (DBT) techniques.", "Practice mindfulness.", "Use a weighted blanket for comfort."],
+            "Moderate Risk": ["Write down emotions and analyze them.", "Engage in creative activities like painting.", "Listen to calming podcasts."],
+            "Low Risk": ["Watch a lighthearted movie.", "Do breathing exercises.", "Call a friend for a short chat."]
+            },
+            "Autism": {
+            "High Risk": ["Engage in deep-pressure therapy (weighted blanket).", "Use noise-canceling headphones.", "Try sensory-friendly relaxation techniques."],
+            "Moderate Risk": ["Do repetitive physical activities like rocking.", "Practice structured breathing exercises.", "Engage in puzzles or memory games."],
+            "Low Risk": ["Spend time in a quiet space.", "Listen to soft instrumental music.", "Follow a structured schedule."]
+            },
+            "Schizophrenia": {
+            "High Risk": ["Seek immediate support from a trusted person.", "Try simple grounding exercises.", "Use distraction techniques like puzzles."],
+            "Moderate Risk": ["Engage in light physical activity.", "Listen to calming sounds or music.", "Write thoughts in a journal."],
+            "Low Risk": ["Read a familiar book.", "Do a 5-minute breathing exercise.", "Try progressive muscle relaxation."]
+            },
+            "Eating Disorders": {
+            "High Risk": ["Seek professional help immediately.", "Try self-affirmations.", "Practice intuitive eating (listen to body cues)."],
+            "Moderate Risk": ["Engage in mindful eating.", "Write down your emotions before meals.", "Do light stretching after meals."],
+            "Low Risk": ["Try a gentle walk after eating.", "Listen to calming music.", "Write a gratitude journal about your body."]
+            },
+            "Mental Health": {
+            "High Risk": ["Reach out to a mental health professional.", "Engage in deep relaxation techniques.", "Talk to a support group."],
+            "Moderate Risk": ["Write in a daily journal.", "Practice guided meditation.", "Do light physical activities like walking."],
+            "Low Risk": ["Try deep breathing exercises.", "Watch an uplifting video.", "Call a friend for a chat."]
+            }
+            
+        }
+        if condition in exercise_recommendations:
+            if risk_level in exercise_recommendations[condition]:
+                return exercise_recommendations[condition][risk_level]
+        return ["No specific recommendations available."]
+    
+
+bot = MentalHealthChatbot()
+
+def analyze_text(text):
+    if not text.strip():
+        return "No input provided.", "", "", "", "", ""
+
+    sentiment, sentiment_conf = bot.get_sentiment(text)
+    disorder, disorder_conf, risk = bot.get_disorder(text)
+
+    if risk == "High Risk":
+        logging.info("🚨 High risk detected! Alert triggered.")
+        alert_msg = "✔ 🚨 Alert Notification Triggered: High risk detected!"
+    else:
+        alert_msg = "✔ Risk is not high. No alert triggered."
+
+    recs = bot.get_recommendations(disorder, risk)
+    lime_explanation = bot.explain_text(text)
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    # Insert data into DB
+    insert_into_database(
+        text,
+        sentiment,
+        sentiment_conf,
+        disorder,
+        disorder_conf,
+        risk,
+        recs,
+        timestamp
+    )
+
+    return (
+        f"{sentiment} ({sentiment_conf:.2f}%)",
+        f"{disorder} ({disorder_conf:.2f}%)",
+        risk,
+        "; ".join(recs),
+        lime_explanation,
+        alert_msg
+    )
+
+# Gradio interface
+gr.Interface(
+    fn=analyze_text,
+    inputs=gr.Textbox(lines=6, label="Describe how you're feeling..."),
+    outputs=[
+        gr.Text(label="Sentiment"),
+        gr.Text(label="Disorder"),
+        gr.Text(label="Risk Level"),
+        gr.Text(label="Recommendations"),
+        gr.Text(label="LIME Explanation"),
+        gr.Text(label="Alert Message")
+    ],
+    title="🧠 Mental Health Analysis Assistant",
+    description="This tool uses AI to detect mental health conditions based on your input and suggest possible actions."
 ).launch()