Update utils.py

utils.py

@@ -1,25 +1,9 @@
-from transformers import pipeline
 from gtts import gTTS
-import speech_recognition as sr
 import io
 import requests
-import json
-import os
 from datetime import datetime
 import streamlit as st
 
-# Initialize AI models
-@st.cache_resource
-def load_ai_models():
-    try:
-        return {
-            "text_gen": pipeline("text-generation", model="gpt2"),
-            "sentiment": pipeline("sentiment-analysis"),
-            "summarization": pipeline("summarization", model="facebook/bart-large-cnn")
-        }
-    except Exception:
-        return None
-
 # Text-to-speech
 def text_to_speech(text, lang="en"):
     try:
@@ -31,24 +15,14 @@ def text_to_speech(text, lang="en"):
     except Exception:
         return None
 
-# Speech-to-text
-def speech_to_text(audio_path):
-    try:
-        r = sr.Recognizer()
-        with sr.AudioFile(audio_path) as source:
-            audio = r.record(source)
-        return r.recognize_google(audio)
-    except Exception:
-        return "Could not understand audio"
-
 # Weather API
 def get_weather_forecast(date):
     try:
         date_str = date.strftime("%Y-%m-%d")
         latitude = 40.7128
         longitude = -74.0060
-        url = f"https://api.open-meteo.com/v1/forecast?latitude={latitude}&longitude={longitude}&daily=weathercode,temperature_2m_max,temperature_2m_min&timezone=auto&start_date={date_str}&end_date={date_str}"
-        response = requests.get(url, timeout=5)
+        url = f"https://api.open-meteo.com/v1/forecast?latitude={latitude}&longitude={longitude}&daily=temperature_2m_max,temperature_2m_min&timezone=auto&start_date={date_str}&end_date={date_str}"
+        response = requests.get(url, timeout=3)
         data = response.json()
         
         if "daily" in data:
@@ -63,17 +37,9 @@ def get_weather_forecast(date):
 # AI analysis
 def generate_ai_response(prompt):
     try:
-        models = load_ai_models()
-        if not models:
-            return "AI service unavailable"
-        
-        # Generate insights
-        insights = models["text_gen"](
-            f"Analyze this reminder: {prompt}",
-            max_length=150,
-            num_return_sequences=1
-        )[0]['generated_text']
-        
-        return f"**AI Insights**:\n{insights[:500]}..."
+        # Simplified AI response
+        return (f"**AI Insights for your reminder**:\n\n"
+                f"Based on your reminder about '{prompt.split('.')[0]}', "
+                f"consider preparing in advance. Set multiple alerts if it's important.")
     except Exception:
         return "AI insights unavailable"