Update app.py

app.py

@@ -2,8 +2,6 @@ import os
 import gradio as gr
 import nltk
 import numpy as np
-import tflearn
-import random
 import json
 import pickle
 from nltk.tokenize import word_tokenize
@@ -13,7 +11,8 @@ import googlemaps
 import folium
 import torch
 import pandas as pd
-from sklearn.tree import DecisionTreeClassifier
+from tensorflow import keras
+from tensorflow.keras import layers
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.naive_bayes import GaussianNB
 from sklearn.metrics import accuracy_score
@@ -34,14 +33,20 @@ with open("intents.json") as file:
 with open("data.pickle", "rb") as f:
     words, labels, training, output = pickle.load(f)
 
-# Build the chatbot model
-net = tflearn.input_data(shape=[None, len(training[0])])
-net = tflearn.fully_connected(net, 8)
-net = tflearn.fully_connected(net, 8)
-net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
-net = tflearn.regression(net)
-chatbot_model = tflearn.DNN(net)
-chatbot_model.load("MentalHealthChatBotmodel.tflearn")
+# Build the chatbot model using Keras
+def build_chatbot_model(input_shape, output_shape):
+    model = keras.Sequential()
+    model.add(layers.Input(shape=(input_shape,)))
+    model.add(layers.Dense(8, activation='relu'))
+    model.add(layers.Dense(8, activation='relu'))
+    model.add(layers.Dense(output_shape, activation='softmax'))
+
+    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+    return model
+
+# Build and train the chatbot model
+chatbot_model = build_chatbot_model(len(training[0]), len(output[0]))
+chatbot_model.fit(training, output, epochs=100)  # Adjust epochs and model fitting parameters as necessary
 
 # Hugging Face sentiment and emotion models
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
@@ -90,7 +95,7 @@ def load_data():
         'Tuberculosis': 25, 
         'Common Cold': 26, 
         'Pneumonia': 27, 
-        'Dimorphic hemorrhoids (piles)': 28,
+        'Dimorphic hemorrhoids (piles)': 28, 
         'Heart attack': 29, 
         'Varicose veins': 30, 
         'Hypothyroidism': 31, 
@@ -108,7 +113,7 @@ def load_data():
     # Replace prognosis values with numerical categories
     df.replace({'prognosis': disease_dict}, inplace=True)
 
-    # Check unique values in prognosis for debugging
+    # Check unique values in prognosis after mapping
     print("Unique values in prognosis after mapping:", df['prognosis'].unique())
 
     # Ensure prognosis is purely numerical after mapping
@@ -358,7 +363,7 @@ with gr.Blocks() as app:
         chatbot = gr.Chatbot(label="Chat History")
         sentiment = gr.Textbox(label="Detected Sentiment")
         emotion = gr.Textbox(label="Detected Emotion")
-
+        
         suggestions = gr.DataFrame(headers=["Title", "Link"])  
         professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])  
         map_html = gr.HTML(label="Interactive Map")