Update app.py

app.py

@@ -11,8 +11,7 @@ import googlemaps
 import folium
 import torch
 import pandas as pd
-from tensorflow import keras
-from tensorflow.keras import layers
+from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.naive_bayes import GaussianNB
 from sklearn.metrics import accuracy_score
@@ -38,15 +37,13 @@ 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.add(layers.Dense(len(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
+chatbot_model.fit(training, output, epochs=100)  # Ensure training data is prepared accordingly
 
 # Hugging Face sentiment and emotion models
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
@@ -113,7 +110,7 @@ def load_data():
     # Replace prognosis values with numerical categories
     df.replace({'prognosis': disease_dict}, inplace=True)
 
-    # Check unique values in prognosis after mapping
+    # Check unique values in prognosis for debugging
     print("Unique values in prognosis after mapping:", df['prognosis'].unique())
 
     # Ensure prognosis is purely numerical after mapping
@@ -314,11 +311,11 @@ def app_function(user_input, location, query, symptoms, history):
     disease_results = disease_prediction_interface(symptoms)
     
     return (
-        chatbot_history, 
-        sentiment_result, 
-        emotion_result, 
-        suggestions, 
-        professionals, 
+        chatbot_history,
+        sentiment_result,
+        emotion_result,
+        suggestions,
+        professionals,
         map_html,
         disease_results
     )