Spaces:

gowthM23
/

_____

Build error

_____ / app.py

Create app.py

25a2b82 verified over 2 years ago

4.4 kB

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from textblob import TextBlob
import language_tool_python
import speech_recognition as sr
import pyttsx3
from gtts import gTTS
import os

# Initialize text-to-speech engine
engine = pyttsx3.init()

# Initialize speech recognition
recognizer = sr.Recognizer()

# Load pre-trained model and tokenizer
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Function to generate a question based on a prompt
def generate_question(prompt):
    inputs = tokenizer.encode(prompt, return_tensors='pt')
    outputs = model.generate(inputs, max_length=50, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
    question = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return question

# Function to get voice input and convert to text
def get_voice_input(prompt):
    # Use TTS to say the prompt
    engine.say(prompt)
    engine.runAndWait()
    
    with sr.Microphone() as source:
        print("Listening...")
        audio = recognizer.listen(source)
        try:
            text = recognizer.recognize_google(audio)
            print(f"User said: {text}")
            return text
        except sr.UnknownValueError:
            print("Sorry, I did not understand that.")
            return None
        except sr.RequestError:
            print("Sorry, there was an error with the speech recognition service.")
            return None

# Function to convert text to speech
def text_to_speech(text):
    tts = gTTS(text=text, lang='en')
    tts.save("response.mp3")
    os.system("start response.mp3")  # For Windows; use "afplay response.mp3" on macOS

# Analyze English using TextBlob
def analyze_english_textblob(text):
    blob = TextBlob(text)
    corrected_text = blob.correct().string
    sentiment = blob.sentiment.polarity  # Range: -1 (negative) to 1 (positive)
    
    feedback = "No significant grammar issues detected."
    if corrected_text != text:
        feedback = "Grammar issues detected."

    # Simple scoring based on sentiment and correction
    score = 100
    if feedback == "Grammar issues detected.":
        score -= 20  # Deduct points for grammar issues

    # Adjust score based on sentiment (optional)
    score += int((sentiment + 1) * 10)  # Normalize sentiment from range [-1,1] to [0, 10]
    score = min(max(score, 0), 100)  # Ensure score is between 0 and 100

    return feedback, score

# Analyze English using LanguageTool
def analyze_english_languagetool(text):
    tool = language_tool_python.LanguageTool('en-US')
    matches = tool.check(text)
    
    feedback = "No issues found."
    issues = []
    
    if matches:
        feedback = f"Issues detected: {len(matches)}"
        for match in matches:
            issues.append(f"{match.ruleId}: {match.message} (Suggestion: {match.replacements})")
    
    # Simple scoring based on number of issues
    score = 100
    if len(matches) > 0:
        score -= len(matches) * 2  # Deduct points for each issue

    return feedback, issues, max(score, 0)  # Ensure score is not below 0

# Main process
def main():
    prompt = "Ask a question about grammar or general knowledge."
    question = generate_question(prompt)
    print("Generated Question:", question)
    
    text_to_speech(f"Question: {question}")
    user_answer = get_voice_input(question)
    
    if user_answer:
        # Limit response to 30 words
        user_answer = ' '.join(user_answer.split()[:30])
        
        # Analyze the response
        feedback_textblob, score_textblob = analyze_english_textblob(user_answer)
        feedback_languagetool, issues, score_languagetool = analyze_english_languagetool(user_answer)
        
        # Calculate average score from both tools
        average_score = (score_textblob + score_languagetool) / 2
        
        # Provide feedback
        feedback = f"{feedback_textblob}. {feedback_languagetool}."
        text_to_speech(f"Your response: {user_answer}. {feedback}. Your score is {average_score}.")
        
        # Print detected issues
        print("\nDetected Issues:")
        for issue in issues:
            print(f" - {issue}")
        print("\nAverage Score:", average_score)
    else:
        text_to_speech("Sorry, I did not catch that. Please try again.")

if __name__ == "__main__":
    main()

	import torch
	from transformers import GPT2LMHeadModel, GPT2Tokenizer
	from textblob import TextBlob
	import language_tool_python
	import speech_recognition as sr
	import pyttsx3
	from gtts import gTTS
	import os

	# Initialize text-to-speech engine
	engine = pyttsx3.init()

	# Initialize speech recognition
	recognizer = sr.Recognizer()

	# Load pre-trained model and tokenizer
	model_name = "gpt2"
	tokenizer = GPT2Tokenizer.from_pretrained(model_name)
	model = GPT2LMHeadModel.from_pretrained(model_name)

	# Function to generate a question based on a prompt
	def generate_question(prompt):
	inputs = tokenizer.encode(prompt, return_tensors='pt')
	outputs = model.generate(inputs, max_length=50, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
	question = tokenizer.decode(outputs[0], skip_special_tokens=True)
	return question

	# Function to get voice input and convert to text
	def get_voice_input(prompt):
	# Use TTS to say the prompt
	engine.say(prompt)
	engine.runAndWait()

	with sr.Microphone() as source:
	print("Listening...")
	audio = recognizer.listen(source)
	try:
	text = recognizer.recognize_google(audio)
	print(f"User said: {text}")
	return text
	except sr.UnknownValueError:
	print("Sorry, I did not understand that.")
	return None
	except sr.RequestError:
	print("Sorry, there was an error with the speech recognition service.")
	return None

	# Function to convert text to speech
	def text_to_speech(text):
	tts = gTTS(text=text, lang='en')
	tts.save("response.mp3")
	os.system("start response.mp3") # For Windows; use "afplay response.mp3" on macOS

	# Analyze English using TextBlob
	def analyze_english_textblob(text):
	blob = TextBlob(text)
	corrected_text = blob.correct().string
	sentiment = blob.sentiment.polarity # Range: -1 (negative) to 1 (positive)

	feedback = "No significant grammar issues detected."
	if corrected_text != text:
	feedback = "Grammar issues detected."

	# Simple scoring based on sentiment and correction
	score = 100
	if feedback == "Grammar issues detected.":
	score -= 20 # Deduct points for grammar issues

	# Adjust score based on sentiment (optional)
	score += int((sentiment + 1) * 10) # Normalize sentiment from range [-1,1] to [0, 10]
	score = min(max(score, 0), 100) # Ensure score is between 0 and 100

	return feedback, score

	# Analyze English using LanguageTool
	def analyze_english_languagetool(text):
	tool = language_tool_python.LanguageTool('en-US')
	matches = tool.check(text)

	feedback = "No issues found."
	issues = []

	if matches:
	feedback = f"Issues detected: {len(matches)}"
	for match in matches:
	issues.append(f"{match.ruleId}: {match.message} (Suggestion: {match.replacements})")

	# Simple scoring based on number of issues
	score = 100
	if len(matches) > 0:
	score -= len(matches) * 2 # Deduct points for each issue

	return feedback, issues, max(score, 0) # Ensure score is not below 0

	# Main process
	def main():
	prompt = "Ask a question about grammar or general knowledge."
	question = generate_question(prompt)
	print("Generated Question:", question)

	text_to_speech(f"Question: {question}")
	user_answer = get_voice_input(question)

	if user_answer:
	# Limit response to 30 words
	user_answer = ' '.join(user_answer.split()[:30])

	# Analyze the response
	feedback_textblob, score_textblob = analyze_english_textblob(user_answer)
	feedback_languagetool, issues, score_languagetool = analyze_english_languagetool(user_answer)

	# Calculate average score from both tools
	average_score = (score_textblob + score_languagetool) / 2

	# Provide feedback
	feedback = f"{feedback_textblob}. {feedback_languagetool}."
	text_to_speech(f"Your response: {user_answer}. {feedback}. Your score is {average_score}.")

	# Print detected issues
	print("\nDetected Issues:")
	for issue in issues:
	print(f" - {issue}")
	print("\nAverage Score:", average_score)
	else:
	text_to_speech("Sorry, I did not catch that. Please try again.")

	if __name__ == "__main__":
	main()