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import os
import sys
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
# Import libraries
import whisper
import gradio as gr
import torch
from transformers import BertTokenizer, BertForSequenceClassification, pipeline
from app.questions import get_question
# patch(niranjan)
try:
# original method
original_method = gr.Blocks.get_api_info
# Create a safer version of the method that catches the specific error
def safe_get_api_info(self):
try:
return original_method(self)
except TypeError as e:
print(f"API info generation error suppressed: {str(e)}", file=sys.stderr)
return {} # Return empty api
gr.Blocks.get_api_info = safe_get_api_info
print("Applied API info generation patch", file=sys.stderr)
except Exception as e:
print(f"Failed to apply patch: {str(e)}", file=sys.stderr)
# Load models
whisper_model = whisper.load_model("small")
confidence_model = BertForSequenceClassification.from_pretrained('RiteshAkhade/final_confidence')
confidence_tokenizer = BertTokenizer.from_pretrained('RiteshAkhade/final_confidence')
context_model = BertForSequenceClassification.from_pretrained('RiteshAkhade/context_model')
context_tokenizer = BertTokenizer.from_pretrained('RiteshAkhade/context_model')
emotion_pipe = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", top_k=1)
# Emotion map with labels and emojis
interview_emotion_map = {
"joy": ("Confident", "๐"),
"fear": ("Nervous", "๐จ"),
"sadness": ("Uncertain", "๐"),
"anger": ("Frustrated", "๐ "),
"surprise": ("Curious", "๐ฎ"),
"neutral": ("Calm", "๐"),
"disgust": ("Disengaged", "๐"),
}
# Static question sets
technical_questions = [get_question(i) for i in range(6)]
non_technical_questions = [
"Tell me about yourself.",
"What are your strengths and weaknesses?",
"Where do you see yourself in 5 years?",
"How do you handle stress or pressure?",
"Describe a time you faced a conflict and how you resolved it.",
"What motivates you to do your best?"
]
# Index trackers
current_tech_index = 0
current_non_tech_index = 0
# Relevance prediction
def predict_relevance(question, answer):
if not answer.strip():
return "Irrelevant"
inputs = context_tokenizer(question, answer, return_tensors="pt", padding=True, truncation=True)
context_model.eval()
with torch.no_grad():
outputs = context_model(**inputs)
probabilities = torch.softmax(outputs.logits, dim=-1)
return "Relevant" if probabilities[0, 1] > 0.5 else "Irrelevant"
# Confidence prediction
def predict_confidence(question, answer, threshold=0.4):
if not isinstance(answer, str) or not answer.strip():
return "Not Confident"
inputs = confidence_tokenizer(question, answer, return_tensors="pt", padding=True, truncation=True)
confidence_model.eval()
with torch.no_grad():
outputs = confidence_model(**inputs)
probabilities = torch.softmax(outputs.logits, dim=-1)
return "Confident" if probabilities[0, 1].item() > threshold else "Not Confident"
# Emotion detection
def detect_emotion(answer):
if not answer.strip():
return "No Answer", ""
result = emotion_pipe(answer)
label = result[0][0]["label"].lower()
emotion_text, emoji = interview_emotion_map.get(label, ("Unknown", "โ"))
return emotion_text, emoji
# Question navigation (non-tech)
def show_non_tech_question():
global current_non_tech_index
return non_technical_questions[current_non_tech_index]
def next_non_tech_question():
global current_non_tech_index
current_non_tech_index = (current_non_tech_index + 1) % len(non_technical_questions)
return non_technical_questions[current_non_tech_index], None, "", ""
# Question navigation (tech)
def show_tech_question():
global current_tech_index
return technical_questions[current_tech_index]
def next_tech_question():
global current_tech_index
current_tech_index = (current_tech_index + 1) % len(technical_questions)
return technical_questions[current_tech_index], None, "", "", ""
# Transcribe + analyze (non-technical)
def transcribe_and_analyze_non_tech(audio, question):
try:
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
result = whisper.decode(whisper_model, mel, whisper.DecodingOptions(fp16=False))
transcribed_text = result.text
emotion_text, emoji = detect_emotion(transcribed_text)
return transcribed_text, f"{emotion_text} {emoji}"
except Exception as e:
return f"Error: {str(e)}", "โ"
# Transcribe + analyze (technical)
def transcribe_and_analyze_tech(audio, question):
try:
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
result = whisper.decode(whisper_model, mel, whisper.DecodingOptions(fp16=False))
transcribed_text = result.text
context_result = predict_relevance(question, transcribed_text)
confidence_result = predict_confidence(question, transcribed_text)
return transcribed_text, context_result, confidence_result
except Exception as e:
return f"Error: {str(e)}", "", ""
# UI layout
with gr.Blocks(css="textarea, .gr-box { font-size: 18px !important; }") as demo:
gr.HTML("<h1 style='text-align: center; font-size: 32px;'>INTERVIEW PREPARATION MODEL</h1>")
with gr.Tabs():
# NON-TECHNICAL TAB
with gr.Tab("Non-Technical"):
gr.Markdown("### Emotional Context Analysis (๐ง + ๐)")
question_display_1 = gr.Textbox(label="Interview Question", value=show_non_tech_question(), interactive=False)
audio_input_1 = gr.Audio(type="filepath", label="Record Your Answer")
transcribed_text_1 = gr.Textbox(label="Transcribed Answer", interactive=False, lines=4)
emotion_output = gr.Textbox(label="Detected Emotion", interactive=False)
audio_input_1.change(fn=transcribe_and_analyze_non_tech,
inputs=[audio_input_1, question_display_1],
outputs=[transcribed_text_1, emotion_output])
next_button_1 = gr.Button("Next Question")
next_button_1.click(fn=next_non_tech_question,
outputs=[question_display_1, audio_input_1, transcribed_text_1, emotion_output])
# TECHNICAL TAB
with gr.Tab("Technical"):
gr.Markdown("### Technical Question Analysis (๐ + ๐ค)")
question_display_2 = gr.Textbox(label="Interview Question", value=show_tech_question(), interactive=False)
audio_input_2 = gr.Audio(type="filepath", label="Record Your Answer")
transcribed_text_2 = gr.Textbox(label="Transcribed Answer", interactive=False, lines=4)
context_analysis_result = gr.Textbox(label="Context Analysis", interactive=False)
confidence_analysis_result = gr.Textbox(label="Confidence Analysis", interactive=False)
audio_input_2.change(fn=transcribe_and_analyze_tech,
inputs=[audio_input_2, question_display_2],
outputs=[transcribed_text_2, context_analysis_result, confidence_analysis_result])
next_button_2 = gr.Button("Next Question")
next_button_2.click(fn=next_tech_question,
outputs=[question_display_2, audio_input_2, transcribed_text_2,
context_analysis_result, confidence_analysis_result])
# Also patch the client utils function that's failing
try:
import gradio_client.utils
# Original function reference
original_json_schema = gradio_client.utils._json_schema_to_python_type
# patched version
def patched_json_schema(schema, defs=None):
try:
if isinstance(schema, bool):
return "bool"
return original_json_schema(schema, defs)
except Exception as e:
print(f"JSON schema conversion error suppressed: {str(e)}", file=sys.stderr)
return "any"
# Apply patch
gradio_client.utils._json_schema_to_python_type = patched_json_schema
print("Applied JSON schema conversion patch", file=sys.stderr)
except Exception as e:
print(f"Failed to apply client utils patch: {str(e)}", file=sys.stderr)
if __name__ == "__main__":
# Simple launch with error handling
try:
demo.launch(show_api=False)
except Exception as e:
print(f"Launch failed: {str(e)}", file=sys.stderr)
try:
demo.launch()
except Exception as e:
print(f"Minimal launch also failed: {str(e)}", file=sys.stderr)
# Create a minimal error app as last resort
with gr.Blocks() as error_app:
gr.Markdown("# Error Starting App")
gr.Markdown("The application encountered errors during startup. Please check the logs.")
error_app.launch() |