Update app.py

app.py

@@ -1,47 +1,33 @@
 import numpy as np
-import gradio as gr
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.text import Tokenizer
 import pickle
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+from sklearn.preprocessing import LabelEncoder
+from keras.models import load_model
+import gradio as gr
 
-# Load the model
+# Load model and encoders
 model = load_model('doctor_ai_model.h5')
-
-# Load the tokenizer
 with open('tokenizer.pkl', 'rb') as f:
     tokenizer = pickle.load(f)
-
-# Load the label encoder
 with open('label_encoder.pkl', 'rb') as f:
     label_encoder = pickle.load(f)
 
-def chatbot(input_text):
-    # Tokenize and pad the input
-    sequences = tokenizer.texts_to_sequences([input_text])
-    input_tensor = tf.keras.preprocessing.sequence.pad_sequences(sequences)
-
-    # Make a prediction
-    response = model.predict(input_tensor)
-    print("Model output probabilities:", response)
-
-    # Get predicted label
-    predicted_label = np.argmax(response, axis=1)
-
-    # Handle unknown labels
-    if predicted_label[0] < len(label_encoder.classes_):
-        decoded_label = label_encoder.inverse_transform(predicted_label)
-    else:
-        decoded_label = "Unknown label"
+# Function to get response from model
+def get_response(input_text):
+    # Preprocess input text
+    input_sequences = tokenizer.texts_to_sequences([input_text])
+    input_tensor = pad_sequences(input_sequences, maxlen=100)  # Adjust maxlen as necessary
 
+    # Make prediction
+    predicted_label = model.predict(input_tensor)
+    decoded_label = label_encoder.inverse_transform([np.argmax(predicted_label)])
+    
     return decoded_label[0]
 
-# Create a Gradio interface
-iface = gr.Interface(fn=chatbot, 
-                     inputs="text", 
-                     outputs="text", 
-                     title="Doctor AI Chatbot",
-                     description="Enter a medical-related question to get answers based on trained categories.")
+# Create Gradio interface
+iface = gr.Interface(fn=get_response, inputs="text", outputs="text", title="Doctor AI", 
+                     description="Ask your health-related questions and get advice!")
 
 # Launch the interface
 iface.launch()