Update app.py

app.py

@@ -1,3 +1,8 @@
+!apt-get update
+!apt-get install -y portaudio19-dev
+!pip install pyaudio gradio datasets transformers torch pandas numpy matplotlib Pillow
+
+from datasets import load_dataset
 import gradio as gr
 import numpy as np
 import pandas as pd
@@ -10,8 +15,7 @@ from typing import Optional, Dict, Any
 import json
 import io
 import os
-import speech_recognition as sr  # Keep this
-from datasets import load_dataset # Moved to top
+import speech_recognition as sr
 
 # Configuration (unchanged)
 INSULIN_TYPES = {
@@ -63,21 +67,19 @@ def load_food_data():
         print(f"Error loading food data: {e}")
         return pd.DataFrame()
 
-# Load model and processor (error handling)
-model_loaded = False
-processor = None
-model = None
 try:
     processor = AutoImageProcessor.from_pretrained("rajistics/finetuned-indian-food")
     model = AutoModelForImageClassification.from_pretrained("rajistics/finetuned-indian-food")
     model_loaded = True
-    print("Model loaded successfully!")  # Add confirmation
 except Exception as e:
     print(f"Model Load Error: {e}")
+    model_loaded = False
+    processor = None
+    model = None
 
 def classify_food(image):
-    if not model_loaded or image is None or processor is None or model is None:  # Check all
-        return "Model not loaded or image is None.  Please check the console for error messages."
+    if not model_loaded or image is None:
+        return "unknown"
     try:
         inputs = processor(images=image, return_tensors="pt")
         with torch.no_grad():
@@ -172,7 +174,7 @@ def insulin_activity(t: float, insulin_type: str, bolus_dose: float, bolus_durat
     return 0
 
 def calculate_active_insulin(insulin_history: list, current_time: float) -> float:
-    return sum(insulin_activity(current_time - dose_time, dose_amount, insulin_type, bolus_duration)
+    return sum(insulin_activity(current_time - dose_time, insulin_type, dose_amount, bolus_duration)
               for dose_time, dose_amount, insulin_type, bolus_duration in insulin_history)
 
 def calculate_insulin_needs(carbs: float, glucose_current: float, glucose_target: float,
@@ -243,20 +245,17 @@ def create_detailed_report(nutrition_info: Dict[str, Any], insulin_info: Dict[st
 
     return glucose_meal_details, insulin_details, basal_details
 
-def speech_to_text(audio_file):  # Keep this
+def speech_to_text(audio_file):
     recognizer = sr.Recognizer()
     try:
         with sr.AudioFile(audio_file) as source:
             audio = recognizer.record(source)
         try:
-            # text = recognizer.recognize_google(audio, language="hi-IN")  # Removed Google
-            text = recognizer.recognize_sphinx(audio)  # Using Sphinx
-
+            text = recognizer.recognize_google(audio, language="hi-IN")
             return HINDI_TO_ENGLISH.get(text.lower(), text)
         except sr.UnknownValueError:
-            return "Sphinx could not understand audio"
-        except sr.RequestError as e:
-            return f"Could not request results from Sphinx service; {e}"
+            text = recognizer.recognize_google(audio, language="en-US")
+            return text
     except Exception as e:
         return f"Error processing audio: {e}"
 
@@ -370,4 +369,4 @@ with gr.Blocks(title="Type 1 Diabetes Management Dashboard") as app:
         outputs=[glucose_meal_output, insulin_output, basal_output, bolus_output, plot_output]
     )
 
-app.launch()
+app.launch(share=True)  # Share=True generates a public URL