Update app.py

app.py

@@ -2,11 +2,13 @@ import gradio as gr
 import pandas as pd
 import lightgbm as lgb
 import numpy as np
-import os
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 from transformers import pipeline, set_seed
 import google.generativeai as genai
+import pyttsx3
+import tempfile
+import os
 
 # ---------------------------
 # Load environment variables
@@ -62,6 +64,18 @@ def smart_chat(user_input, model_choice):
     else:
         return chat_with_bot(user_input)
 
+# ---------------------------
+# Text-to-Speech Function
+# ---------------------------
+def text_to_speech(text):
+    # Initialize the pyttsx3 engine
+    engine = pyttsx3.init()
+    # Save the audio to a temporary file
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
+    engine.save_to_file(text, temp_file.name)
+    engine.runAndWait()
+    return temp_file.name
+
 # ---------------------------
 # GRADIO APP UI
 # ---------------------------
@@ -111,7 +125,12 @@ with gr.Blocks() as demo:
             chatbot_output = gr.Textbox(label="AgroVision Bot Response")
             chatbot_btn = gr.Button("Ask")
 
-            chatbot_btn.click(fn=smart_chat, inputs=[user_input, model_selector], outputs=chatbot_output)
+            def respond_with_audio_and_text(user_input, model_choice):
+                response_text = smart_chat(user_input, model_choice)
+                audio_file = text_to_speech(response_text)
+                return response_text, audio_file
+
+            chatbot_btn.click(fn=respond_with_audio_and_text, inputs=[user_input, model_selector], outputs=[chatbot_output, audio_input])
 
 # Launch the app
 demo.launch()