Update utils.py

utils.py

@@ -2,52 +2,78 @@ from transformers import pipeline
 from gtts import gTTS
 import speech_recognition as sr
 import io
-import torch
-import numpy as np
+import requests
+import json
+import os
+from datetime import datetime
+import streamlit as st
 
 # Initialize AI models
 @st.cache_resource
 def load_ai_models():
-    return {
-        "text_gen": pipeline("text-generation", model="gpt2"),
-        "sentiment": pipeline("sentiment-analysis"),
-        "summarization": pipeline("summarization", model="facebook/bart-large-cnn")
-    }
+    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"):
-    tts = gTTS(text=text, lang=lang)
-    audio_bytes = io.BytesIO()
-    tts.write_to_fp(audio_bytes)
-    return audio_bytes.getvalue()
+    try:
+        tts = gTTS(text=text, lang=lang)
+        audio_bytes = io.BytesIO()
+        tts.write_to_fp(audio_bytes)
+        audio_bytes.seek(0)
+        return audio_bytes
+    except Exception:
+        return None
 
 # Speech-to-text
 def speech_to_text(audio_path):
-    r = sr.Recognizer()
-    with sr.AudioFile(audio_path) as source:
-        audio = r.record(source)
-    return r.recognize_google(audio)
+    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)
+        data = response.json()
+        
+        if "daily" in data:
+            temp_max = data["daily"]["temperature_2m_max"][0]
+            temp_min = data["daily"]["temperature_2m_min"][0]
+            return f"High: {temp_max}°C, Low: {temp_min}°C"
+        
+        return "Weather data unavailable"
+    except Exception:
+        return "Weather service unavailable"
 
 # AI analysis
 def generate_ai_response(prompt):
-    models = load_ai_models()
-    
-    # Generate insights
-    insights = models["text_gen"](
-        f"Analyze this reminder and provide helpful suggestions: {prompt}",
-        max_length=150,
-        num_return_sequences=1
-    )[0]['generated_text']
-    
-    # Sentiment analysis
-    sentiment = models["sentiment"](prompt[:512])[0]
-    
-    # Summarization
-    summary = models["summarization"](
-        insights,
-        max_length=80,
-        min_length=30,
-        do_sample=False
-    )[0]['summary_text']
-    
-    return f"**AI Insights**: {summary}\n\n**Sentiment**: {sentiment['label']} ({sentiment['score']:.2f})"
+    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]}..."
+    except Exception:
+        return "AI insights unavailable"