Update app.py

app.py

@@ -1,21 +1,129 @@
+from botocore.exceptions import BotoCoreError, ClientError
+from contextlib import closing
+import os
+import urllib
+import boto3
 import gradio as gr
 from openai import OpenAI
 import os
+import time
+import json
 from dotenv import load_dotenv
-import azure.cognitiveservices.speech as speechsdk
-import wave
+
 
 # Loads and set environment variables
 load_dotenv(".env")
 api_key = os.getenv("OPENAI_API_KEY")
-speech_key = os.getenv("speech_key")
-os.environ['SPEECH_REGION'] = 'eastus'
 client = OpenAI(api_key=api_key)
+access_key = os.getenv('access_key')
+secret_key = os.getenv('secret_key')
+
+
+def recognize_from_microphone(file_info):
+    if not file_info:
+        return "No audio file received.", ""
+    file_path = file_info
+    print(f"File path received: {file_path}")
+
+    # Check file existence
+    if not os.path.exists(file_path):
+        return f"File not found: {file_path}", ""
+
+    # Configuring Amazon Transcribe
+    transcribe_client = boto3.client('transcribe', region_name='us-east-1', aws_access_key_id=access_key, aws_secret_access_key=secret_key)
+    s3_client = boto3.client('s3', region_name='us-west-2', aws_access_key_id=access_key, aws_secret_access_key=secret_key)
+    bucket_name = 'nutrition-bot'  # Specify your S3 bucket name
+    object_name = os.path.basename(file_path)
+
+    # Upload file to S3
+    s3_client.upload_file(file_path, bucket_name, object_name)
+    job_name = f"TranscriptionJob-{int(time.time())}"
+    job_uri = f"s3://{bucket_name}/{object_name}"
+
+    # Start transcription job
+    transcribe_client.start_transcription_job(
+        TranscriptionJobName=job_name,
+        Media={'MediaFileUri': job_uri},
+        MediaFormat='mp3',  # or your file format, e.g., wav
+        LanguageCode='en-US'
+    )
+
+    # Checking job status
+    while True:
+        status = transcribe_client.get_transcription_job(TranscriptionJobName=job_name)
+        if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']:
+            break
+        time.sleep(5)
+
+    # Process the transcription result
+    if status['TranscriptionJob']['TranscriptionJobStatus'] == 'COMPLETED':
+        transcript_uri = status['TranscriptionJob']['Transcript']['TranscriptFileUri']
+        transcript_response = urllib.request.urlopen(transcript_uri)
+        transcript_data = json.load(transcript_response)
+        transcript_text = transcript_data['results']['transcripts'][0]['transcript']
+        return transcript_text, ""
+    return "Failed to transcribe audio.", ""
+
+
+def synthesize_speech(text, filename="output.mp3"):
+    """Converts text to speech using Amazon Polly and saves it to an MP3 file."""
+    # Create a Polly client
+    polly_client = boto3.client('polly', region_name='us-east-1', aws_access_key_id=access_key, aws_secret_access_key=secret_key)
+
+    # Synthesize speech
+    response = polly_client.synthesize_speech(
+        Text=text,
+        OutputFormat='mp3',  # MP3 output format
+        VoiceId='Salli'     # Using Joanna voice, you can choose another
+    )
+
+    # Accessing the audio stream from the response
+    if "AudioStream" in response:
+        with open(filename, 'wb') as file:
+            file.write(response['AudioStream'].read())
+        print(f"Speech synthesized for text [{text}] and saved to {filename}")
+    else:
+        print(f"Failed to synthesize speech for text [{text}]")
+
+    return filename
+
+
 
+def chatbot_response(user_input="", gender=None, plan_type=None, weight=None, height=None, audio_input=None):
+    transcription, response = "", ""  # Initialize variables for transcription and response
+    error_message = ""  # Initialize error_message at the start of the function
 
+    if audio_input:
+        transcription, error = recognize_from_microphone(audio_input)
+        if error:
+            error_message = error  # Capture the error to return it properly
+        else:
+            user_input = transcription  # Use the transcription if there's no error
 
+    # Check if there's user input or transcription, and there's no error message
+    if not user_input.strip() and not transcription.strip() and not error_message:
+        error_message = "Please provide audio input or type your question."
+
+    if error_message:
+        return error_message, ""  # Return the error with an empty second value
+
+    # Process user_input as before (assuming previous code handled it)
+    detailed_input = f"User details - Gender: {gender}, Plan Type: {plan_type}, Weight: {weight} kg, Height: {height} cm. Question: {user_input}"
+    try:
+        completion = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": "You are a nutrition consultant AI, capable of providing natural diet plans and emergency assistance based on user inputs."},
+                {"role": "user", "content": detailed_input},
+            ]
+        )
+        response = completion.choices[0].message.content
+        if response:
+            audio_path = synthesize_speech(response)
+            return transcription, response, audio_path  # Return audio path along with text and transcription
+    except Exception as e:
+        return transcription, f"An error occurred during response generation: {str(e)}"  # Return both values even in case of an exception
 
-    
 
 def emergency_assistance(query):
     if not query.strip():
@@ -29,19 +137,40 @@ def emergency_assistance(query):
             ]
         )
         response = completion.choices[0].message.content
-        return response
     except Exception as e:
         return f"An error occurred: {str(e)}"
+    # After generating response:
+    if response:
+        audio_path = synthesize_speech(response)
+        return response, audio_path  # Return both response text and audio path
 
 
-
-
+# Adjust Gradio interfaces to include audio output
+interface1 = gr.Interface(
+    fn=chatbot_response,
+    inputs=[
+        gr.Textbox(lines=5, label="Input Here", placeholder="Type or say your question here..."),
+        gr.Radio(choices=["Male", "Female", "Other"], label="Gender"),
+        gr.Radio(choices=["Weight Gain", "Weight Loss"], label="Plan Type"),
+        gr.Number(label="Weight (kg)", info="Enter your weight in kg"),
+        gr.Number(label="Height (cm)", info="Enter your height in cm"),
+        gr.Audio(type="filepath", label="Record your question")
+        ],
+    outputs=[
+        gr.Text(label="Transcription"),
+        gr.Text(lines=10, label="Response"),
+        gr.Audio(label="Listen to Response")  # New audio output for the synthesized speech
+    ],
+    title="Personalized Nutrition AI Advisor",
+    description="Ask me anything about nutrition. Provide your Gender, Plan Type, Weight and Height for personalized advice."
+)
 
 interface2 = gr.Interface(
     fn=emergency_assistance,
     inputs=[gr.Textbox(lines=10, label="Query", placeholder="Enter your emergency nutrition query here...")],
     outputs=[
         gr.Text(lines=10, label="Response"),
+        gr.Audio(label="Listen to Response")  # New audio output for the synthesized speech
     ],
     title="Emergency Assistance",
     description="To better assist you, could you explain what led to this emergency?"
@@ -49,7 +178,7 @@ interface2 = gr.Interface(
 
 
 # Combined interface with tabs
-app = gr.TabbedInterface([interface2], ["Nutrition Consultant", "Emergency Assistance"], title="HealthyBytes: Your AI Nutrition Consultant")
+app = gr.TabbedInterface([interface1, interface2], ["Nutrition Consultant", "Emergency Assistance"], title="HealthyBytes: Your AI Nutrition Consultant")
 
 if __name__ == "__main__":
-    app.launch(share=False)
+    app.launch(share=False)