Update app.py

app.py

@@ -1,33 +1,26 @@
 import gradio as gr
 import pandas as pd
 import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, T5Tokenizer, T5ForConditionalGeneration
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 from sentence_transformers import SentenceTransformer, util
 
 # ------------------------------
 # Load Models
 # ------------------------------
-# Fine-tuned quiz generator model
-quiz_model_name = "iarfmoose/t5-base-question-generator"
-quiz_tokenizer = AutoTokenizer.from_pretrained(quiz_model_name)
-quiz_model = AutoModelForSeq2SeqLM.from_pretrained(quiz_model_name)
-
-# For summarizer and fallback tasks
-default_model_name = "t5-base"
-tokenizer_qg = T5Tokenizer.from_pretrained(default_model_name)
-model_qg = T5ForConditionalGeneration.from_pretrained(default_model_name)
-
-# For plagiarism detection
+qg_model_name = "iarfmoose/t5-base-question-generator"
+tokenizer_qg = AutoTokenizer.from_pretrained(qg_model_name)
+model_qg = AutoModelForSeq2SeqLM.from_pretrained(qg_model_name)
 model_plag = SentenceTransformer('all-MiniLM-L6-v2')
+asr = pipeline("automatic-speech-recognition", model="openai/whisper-base")
 
 # ------------------------------
-# Quiz Generator (Fine-Tuned)
+# Quiz Generator
 # ------------------------------
 def generate_mcqs(text, num_questions=3):
     input_text = f"generate questions: {text.strip()}"
-    input_ids = quiz_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    input_ids = tokenizer_qg.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
 
-    outputs = quiz_model.generate(
+    outputs = model_qg.generate(
         input_ids=input_ids,
         max_length=256,
         num_return_sequences=num_questions,
@@ -36,7 +29,7 @@ def generate_mcqs(text, num_questions=3):
         top_p=0.95
     )
 
-    questions = [quiz_tokenizer.decode(out, skip_special_tokens=True).strip() for out in outputs]
+    questions = [tokenizer_qg.decode(out, skip_special_tokens=True).strip() for out in outputs]
     return "\n".join([f"{i+1}. {q}" for i, q in enumerate(questions)])
 
 # ------------------------------
@@ -54,10 +47,15 @@ def chatbot_response(message, history):
     return "This is a placeholder response for now. (LLM not integrated)"
 
 # ------------------------------
-# Speech Question Solver (Mock)
+# Speech Question Solver (NEW)
 # ------------------------------
-def speech_answer(audio):
-    return "Audio transcription and answer generation not supported offline."
+def speech_answer(audio_file_path):
+    transcription = asr(audio_file_path)["text"]
+    input_text = f"generate questions: {transcription.strip()}"
+    input_ids = tokenizer_qg.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model_qg.generate(input_ids, max_length=256, num_return_sequences=1)
+    response = tokenizer_qg.decode(outputs[0], skip_special_tokens=True)
+    return f"🗣️ Transcript: {transcription.strip()}\n\n💡 Answer: {response.strip()}"
 
 # ------------------------------
 # Summarizer
@@ -108,7 +106,7 @@ def check_plagiarism(text1, text2):
 # Gradio Interface
 # ------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("# 📚 Smart LMS Suite (AI Powered Offline Tools)")
+    gr.Markdown("# 📚 Smart LMS Suite (Offline)")
 
     with gr.Tab("🧠 Quiz Generator"):
         quiz_text = gr.Textbox(label="📄 Input Content", lines=6, placeholder="Paste a paragraph here...")
@@ -128,7 +126,7 @@ with gr.Blocks() as demo:
 
     with gr.Tab("🎤 Speech Q Solver"):
         audio_in = gr.Audio(label="Upload Audio", type="filepath")
-        audio_btn = gr.Button("Get Answer")
+        audio_btn = gr.Button("Transcribe + Generate Answer")
         audio_out = gr.Textbox(label="Answer")
         audio_btn.click(fn=speech_answer, inputs=audio_in, outputs=audio_out)
 
@@ -165,6 +163,6 @@ with gr.Blocks() as demo:
         plag_btn.click(fn=check_plagiarism, inputs=[text1, text2], outputs=plag_out)
 
 # ------------------------------
-# Launch the App
+# Launch
 # ------------------------------
 demo.launch()