added main file

main.py

@@ -0,0 +1,61 @@
+import streamlit as st
+from transformers import pipeline
+import json
+import os
+
+# Initialize HuggingFace model (text generation)
+@st.cache_resource
+def load_generator():
+    return pipeline("text-generation", model="gpt2")
+
+generator = load_generator()
+
+# JSON file to store user data
+DATA_FILE = "user_data.json"
+
+def load_data():
+    if os.path.exists(DATA_FILE):
+        with open(DATA_FILE, "r") as f:
+            return json.load(f)
+    return []
+
+def save_data(data):
+    with open(DATA_FILE, "w") as f:
+        json.dump(data, f, indent=2)
+
+def generate_python_question(difficulty):
+    prompt = f"Generate a {difficulty} level Python coding test question."
+    output = generator(prompt, max_length=100, num_return_sequences=1)[0]["generated_text"]
+    return output.split("\n")[0].strip()  # Get only the first line
+
+# Streamlit UI
+st.title("🧠 Python Test Generator with HuggingFace")
+st.markdown("Generate Python test questions and save user data")
+
+name = st.text_input("Enter your name")
+difficulty = st.selectbox("Choose difficulty level", ["easy", "medium", "hard"])
+
+if st.button("Generate Question"):
+    if not name:
+        st.warning("Please enter your name.")
+    else:
+        question = generate_python_question(difficulty)
+        st.session_state["question"] = question
+
+# Display generated question
+if "question" in st.session_state:
+    st.subheader("Your Python Question:")
+    st.write(st.session_state["question"])
+    
+    answer = st.text_area("Your Answer")
+    if st.button("Submit Answer"):
+        user_entry = {
+            "name": name,
+            "difficulty": difficulty,
+            "question": st.session_state["question"],
+            "answer": answer
+        }
+        data = load_data()
+        data.append(user_entry)
+        save_data(data)
+        st.success("Answer submitted and saved!")