Update app.py

app.py

@@ -1,90 +1,85 @@
-# Install necessary libraries
-
-import os
-import whisper
-from gtts import gTTS
-import gradio as gr
 import streamlit as st
+import gradio as gr
+import requests
+import openai
+from gtts import gTTS
+import soundfile as sf
 from transformers import pipeline
-from groq import Groq
-from datasets import load_dataset
-
-# Load career guidance dataset from Hugging Face
-career_guidance_dataset = load_dataset("mb7419/career-guidance-reddit")
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+import io
 
-# Initialize Whisper model
-whisper_model = whisper.load_model("large")
+# Setting up API keys
+openai.api_key = "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"
+groq_api_key = "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"
 
-# Initialize Groq client and LLaMA model
-GROQ_API_KEY = "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"
-client = Groq(api_key=GROQ_API_KEY)
-llama_pipeline = pipeline("text-generation", model="tgkamal/student_career_path-llama")
+# Load Whisper model for voice-to-text
+whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
+whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-large")
 
-# Function to convert text to speech
-def text_to_speech(text):
-    tts = gTTS(text=text, lang='en')
-    response_audio_path = "response.mp3"
-    tts.save(response_audio_path)
-    return response_audio_path
+# Load LLaMA model for language processing
+llama_model = pipeline("text-generation", model="sujal011/llama3.2-3b-disease-symptoms", device=0)
 
-# Function to transcribe audio to text using Whisper
-def transcribe_audio(audio_path):
-    result = whisper_model.transcribe(audio_path)
-    return result["text"]
+# Function to convert voice input to text
+def audio_to_text(audio_file):
+    audio_input, _ = sf.read(audio_file)
+    input_features = whisper_processor(audio_input, return_tensors="pt").input_features
+    generated_ids = whisper_model.generate(input_features)
+    transcription = whisper_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return transcription
 
-# Function to generate career counseling response using LLaMA model
-def get_counseling_response(user_input):
-    messages = [{"role": "user", "content": user_input}]
-    response = llama_pipeline(messages)
-    return response[0]["generated_text"]
+# Function to handle user input
+def handle_user_input(user_text):
+    # Call LLaMA model to process input and generate response
+    response = llama_model(user_text, max_length=150)
+    return response[0]['generated_text']
 
-# Function to ask quiz questions based on the user's background
-def generate_quiz(education_level, skills):
-    # Sample questions based on educational background and skills
-    questions = [
-        f"What is your experience level in {skills[0]}?",
-        f"How would you rate your proficiency in {skills[1]}?",
-        "Which field do you prefer working in: technology, business, or academia?",
-        "Do you enjoy research-oriented work or practical applications?",
-        # Add more relevant questions here up to 20
-    ]
-    return questions
+# Function to generate audio from text
+def text_to_audio(text):
+    tts = gTTS(text)
+    audio_bytes = io.BytesIO()
+    tts.save(audio_bytes)
+    audio_bytes.seek(0)
+    return audio_bytes
 
-# Main function to handle chatbot interaction
-def career_chatbot(audio_path):
-    # Step 1: Transcribe audio to text
-    user_input = transcribe_audio(audio_path)
-    st.write(f"User said: {user_input}")
-    
-    # Step 2: Generate a career counseling response based on quiz results
-    response_text = get_counseling_response(user_input)
-    st.write(f"Career Counseling Bot says: {response_text}")
-    
-    # Step 3: Convert response text to audio
-    response_audio_path = text_to_speech(response_text)
-    
-    # Return text and audio response paths
-    return response_text, response_audio_path
-
-# Streamlit and Gradio Interface Integration
+# Streamlit UI setup
 st.title("Career Counseling Chatbot")
-st.write("Ask career-related questions and receive guidance based on your background.")
+st.write("Answer questions to receive personalized career advice.")
+
+# Input method selection
+input_method = st.radio("Choose your input method:", ("Text", "Voice"))
 
-# Audio input using Gradio in Streamlit
-with gr.Blocks() as gr_interface:
-    gr.Audio(type="filepath", label="Speak to the Career Bot")
-    gr.outputs=["text", gr.Audio(type="filepath")]
+# Collect educational background, skills, and aspirations
+st.subheader("Tell us about your educational background and skills.")
+education_level = st.selectbox("Education Level", ["Intermediate", "Undergraduate", "Graduate"])
+technical_skills = st.text_area("Technical Skills", "E.g., Python, Machine Learning, etc.")
+soft_skills = st.text_area("Soft Skills", "E.g., Communication, Leadership, etc.")
+future_aspirations = st.text_area("Future Career Interests", "E.g., Data Scientist, Software Developer, etc.")
 
-# Set up Streamlit's interactive components for quiz
-education_level = st.selectbox("Select your education level:", ["Intermediate", "Undergraduate", "Graduate"])
-skills = st.multiselect("Select your technical skills:", ["Python", "Machine Learning", "Data Analysis", "Web Development"])
+# Conduct quiz
+st.subheader("Career Aptitude Quiz")
+quiz_questions = [
+    "Question 1: Your interest in scientific research?",
+    "Question 2: Your comfort with technology?",
+    # Add more questions as needed
+]
+quiz_responses = []
+for question in quiz_questions:
+    response = st.radio(question, ["High", "Moderate", "Low"])
+    quiz_responses.append(response)
 
-if st.button("Start Quiz"):
-    quiz_questions = generate_quiz(education_level, skills)
-    user_responses = {}
-    for question in quiz_questions:
-        user_responses[question] = st.text_input(question)
-    st.write("Thank you for answering the questions!")
+# Process input and provide career suggestion
+if input_method == "Text":
+    user_input = st.text_input("Enter your query:")
+    if user_input:
+        response = handle_user_input(user_input)
+        st.write("Chatbot:", response)
+        st.audio(text_to_audio(response), format="audio/mp3")
 
-# Connect Streamlit and Gradio to launch the chatbot
-gr_interface.launch(share=True)
+elif input_method == "Voice":
+    user_audio = st.file_uploader("Upload your voice query:", type=["wav", "mp3"])
+    if user_audio:
+        transcription = audio_to_text(user_audio)
+        response = handle_user_input(transcription)
+        st.write("You said:", transcription)
+        st.write("Chatbot:", response)
+        st.audio(text_to_audio(response), format="audio/mp3")