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import os
from fastapi import FastAPI, Request, Form, File, UploadFile
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.templating import Jinja2Templates
from groq import Groq
import io
# Set up the Groq client
os.environ["GROQ_API_KEY"] = "your_groq_api_key_here"
client = Groq(api_key=os.environ["GROQ_API_KEY"])
# Initialize FastAPI app and template engine
app = FastAPI()
templates = Jinja2Templates(directory="templates")
@app.get("/", response_class=HTMLResponse)
async def index(request: Request):
return templates.TemplateResponse("index.html", {"request": request})
@app.post("/transcribe")
async def transcribe_audio(audio_data: UploadFile = File(...), language: str = Form(...)):
try:
audio_content = await audio_data.read()
# Transcribe the audio based on the selected language
transcription = client.audio.transcriptions.create(
file=(audio_data.filename, audio_content),
model="whisper-large-v3",
prompt="Transcribe the audio accurately based on the selected language.",
response_format="text",
language=language,
)
return JSONResponse(content={'transcription': transcription})
except Exception as e:
return JSONResponse(status_code=500, content={'error': str(e)})
@app.post("/check_grammar")
async def check_grammar(transcription: str = Form(...), language: str = Form(...)):
if not transcription or not language:
return JSONResponse(status_code=400, content={'error': 'Missing transcription or language selection'})
try:
# Grammar check
grammar_prompt = (
f"Briefly check the grammar of the following text in {language}: {transcription}. "
"Identify any word that does not belong to the selected language and flag it. "
"Based on the number of incorrect words also check the grammar deeply and carefully. "
"Provide a score from 1 to 10 based on the grammar accuracy, reducing points for incorrect words and make sure to output the score on a new line after two line breaks like 'SCORE='."
)
grammar_check_response = client.chat.completions.create(
model="llama3-groq-70b-8192-tool-use-preview",
messages=[{"role": "user", "content": grammar_prompt}]
)
grammar_feedback = grammar_check_response.choices[0].message.content.strip()
# Vocabulary check
vocabulary_prompt = (
f"Check the vocabulary accuracy of the following text in {language}: {transcription}. "
"Identify any word that does not belong to the selected language and flag it. "
"Based on the number of incorrect words also check the grammar deeply and carefully. "
"Provide a score from 1 to 10 based on the vocabulary accuracy reducing points for incorrect words and make sure to output the score on a new line after two line breaks like 'SCORE='."
)
vocabulary_check_response = client.chat.completions.create(
model="llama-3.1-70b-versatile",
messages=[{"role": "user", "content": vocabulary_prompt}]
)
vocabulary_feedback = vocabulary_check_response.choices[0].message.content.strip()
# Calculate scores
grammar_score = calculate_score(grammar_feedback)
vocabulary_score = calculate_score(vocabulary_feedback)
return JSONResponse(content={
'grammar_feedback': grammar_feedback,
'vocabulary_feedback': vocabulary_feedback,
'grammar_score': grammar_score,
'vocabulary_score': vocabulary_score
})
except Exception as e:
return JSONResponse(status_code=500, content={'error': str(e)})
def calculate_score(feedback: str) -> int:
"""
Calculate score based on feedback content.
This function searches for the keyword 'SCORE=' or similar variations
(SCORE:, score:, etc.) and extracts the score value.
"""
import re
match = re.search(r'(SCORE=|score=|SCORE:|score:|SCORE = )\s*(\d+)', feedback)
if match:
return int(match.group(2))
return 0 # Return default score of 0 if no score is found
# Run the FastAPI app (only needed for local development)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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