Add application file

app.py

@@ -0,0 +1,92 @@
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import torch
+import gradio as gr
+import re
+
+model_path = "prajjwal888/Llama-2-7b-chat-question-generation"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+def parse_generated_text(text: str) -> dict:
+    clean_text = re.sub(r"\[/?INST\]", "", text)
+    clean_text = re.sub(r"Question:\s*Question:", "Question:", clean_text)
+    clean_text = clean_text.strip()
+
+    match = re.search(r"Question:\s*(.*?)(?:\nHint:|Hint:)(.*)", clean_text, re.DOTALL)
+
+    if match:
+        question = match.group(1).strip().strip('"').replace("Question:", "").strip()
+        hint = match.group(2).strip().strip('"')
+    else:
+        question = clean_text.strip()
+        hint = "No hint available"
+
+    return {
+        "question": question,
+        "hint": hint
+    }
+
+def generate_questions(topic, difficulty, types, count):
+    print("Received input:", topic, difficulty, types, count)  
+
+    try:
+        pipe = pipeline(
+            task="text-generation",
+            model=model,
+            tokenizer=tokenizer,
+            # device=0,
+            max_length=200,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True
+        )
+
+        questions = []
+
+        for _ in range(count):
+            for q_type in types:
+                prompt = (
+                    f"Generate a {difficulty} difficulty {q_type} question about {topic}.\n"
+                    "Format strictly as follows:\n"
+                    "Question: <your question here>\n"
+                    "Hint: <your hint here or 'No hint available'>"
+                )
+
+                formatted_prompt = f"<s>[INST] {prompt} [/INST]"
+                print("Prompt:", formatted_prompt)
+
+                result = pipe(formatted_prompt)
+                print("Raw Output:", result)
+
+                generated_text = result[0]['generated_text'].replace(formatted_prompt, "").strip()
+                parsed = parse_generated_text(generated_text)
+
+                questions.append(f"**Type**: {q_type}\n\n**Question**: {parsed['question']}\n\n**Hint**: {parsed['hint']}\n\n---")
+
+        return "\n\n".join(questions)
+
+    except Exception as e:
+        print("Error:", e)
+        return f"Something went wrong: {e}"
+
+iface = gr.Interface(
+    fn=generate_questions,
+    inputs=[
+        gr.Textbox(label="Topic"),
+        gr.Dropdown(choices=["easy", "medium", "hard"], label="Difficulty", value="medium"),
+        gr.CheckboxGroup(choices=["Conceptual", "Numerical", "Application"], label="Question Types"),
+        gr.Slider(minimum=1, maximum=5, step=1, value=2, label="Number of Questions per Type")
+    ],
+    outputs=gr.Markdown(label="Generated Questions"),
+    title="AI Question Generator",
+    description="Enter a topic, select difficulty and question types to generate AI-powered questions."
+)
+
+iface.queue()
+iface.launch()