Create app.py

app.py

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+import os
+import gradio as gr
+import docx2txt
+import PyPDF2
+import csv
+from groq import Groq
+
+# Initialize Groq API client
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+
+def extract_text_from_file(file):
+    """
+    Extracts text from a PDF or Word document file.
+    """
+    text = ""
+    if file.name.endswith(".pdf"):
+        pdf_reader = PyPDF2.PdfReader(file)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    elif file.name.endswith(".docx"):
+        text = docx2txt.process(file.name)
+    return text
+
+def classify_and_generate_questions(text_chunk, question_type="MCQ"):
+    """
+    Uses Groq's Llama model to classify content and generate specific types of questions.
+    """
+    # Define prompt templates for each question type
+    question_prompts = {
+        "MCQ": f"Based on the following content, generate a multiple-choice question (MCQ) with four answer options. Specify the correct answer.\n\nText:\n{text_chunk}\n\nResponse:",
+        "Fill-in-the-Blank": f"Based on the following content, create a fill-in-the-blank question by leaving out an important word or concept.\n\nText:\n{text_chunk}\n\nResponse:",
+        "Short Answer": f"Based on the following content, generate a short answer question.\n\nText:\n{text_chunk}\n\nResponse:",
+        "Concept Explanation": f"Based on the following content, create a question that requires explaining the concept in detail.\n\nText:\n{text_chunk}\n\nResponse:",
+        "Numerical": f"Based on the following content, create a numerical question. Provide the correct answer if possible.\n\nText:\n{text_chunk}\n\nResponse:",
+    }
+
+    # Select the prompt based on the question type
+    prompt = question_prompts.get(question_type, question_prompts["MCQ"])
+    
+    # Perform chat completion using Groq's Llama model
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": prompt
+            }
+        ],
+        model="llama3-8b-8192"  # Replace with the model name you have access to
+    )
+    
+    # Extract the response content
+    return chat_completion.choices[0].message.content
+
+def generate_questions(file, question_type, save_format):
+    """
+    Main function to process the file, generate questions, and optionally save them.
+    """
+    # Extract text
+    text = extract_text_from_file(file)
+    if not text:
+        return "Could not extract text from the document.", None
+
+    # Split text into chunks to handle model limits
+    chunk_size = 500
+    text_chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
+
+    # Generate questions for each chunk
+    questions = []
+    for chunk in text_chunks:
+        question = classify_and_generate_questions(chunk, question_type=question_type)
+        questions.append(question)
+
+    # Format questions for display
+    questions_display = "\n\n".join(questions)
+
+    # Save questions if selected
+    if save_format == "Text File":
+        with open("generated_questions.txt", "w") as f:
+            for question in questions:
+                f.write(question + "\n\n")
+        return questions_display, "generated_questions.txt"
+
+    elif save_format == "CSV File":
+        with open("generated_questions.csv", "w", newline="") as f:
+            writer = csv.writer(f)
+            writer.writerow(["Question"])
+            for question in questions:
+                writer.writerow([question])
+        return questions_display, "generated_questions.csv"
+
+    return questions_display, None
+
+# Gradio Interface
+file_input = gr.inputs.File(label="Upload PDF or Word Document")
+question_type = gr.inputs.Dropdown(["MCQ", "Fill-in-the-Blank", "Short Answer", "Concept Explanation", "Numerical"], label="Question Type")
+save_format = gr.inputs.Dropdown(["None", "Text File", "CSV File"], label="Save Questions As")
+output_text = gr.outputs.Textbox(label="Generated Questions")
+output_file = gr.outputs.File(label="Download File")
+
+gr.Interface(
+    fn=generate_questions,
+    inputs=[file_input, question_type, save_format],
+    outputs=[output_text, output_file],
+    title="Engineering Quiz Question Generator",
+    description="Upload a PDF or Word document, choose the type of quiz question, and save them to a file if desired."
+).launch()