Update app.py

app.py

@@ -1,129 +1,129 @@
-import streamlit as st
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
-from langchain_google_genai import ChatGoogleGenerativeAI
-import fitz
-import json
-
-# Title
-st.title("📄 PDF-based MCQ Generator")
-
-# Sidebar
-st.sidebar.title("Upload & Settings")
-
-# Upload PDF
-pdf_file = st.sidebar.file_uploader("Upload a PDF file", type=["pdf"])
-
-# Number of questions
-number_of_questions = st.sidebar.slider("Number of questions", min_value=1, max_value=20, value=5)
-
-# Session states
-if "mcqs" not in st.session_state:
-    st.session_state.mcqs = []
-if "current_q" not in st.session_state:
-    st.session_state.current_q = 0
-if "user_answers" not in st.session_state:
-    st.session_state.user_answers = {}
-if "quiz_finished" not in st.session_state:
-    st.session_state.quiz_finished = False
-
-# Gemini setup
-GOOGLE_API_KEY = "AIzaSyCB5NLx39vOAlfRQBDmnEG3uLBgLraGvH4"
-llm = ChatGoogleGenerativeAI(
-    model="gemini-2.0-flash",
-    google_api_key=GOOGLE_API_KEY,
-    temperature=0.7
-)
-
-template = """
-You are an expert MCQ generator. Generate {number} unique multiple-choice questions from the given text.
-Each question must have exactly 1 correct answer and 3 incorrect options.
-Strictly return output in the following JSON format (no explanations, no markdown):
-
-[
-  {{
-    "question": "What is ...?",
-    "options": ["Option A", "Option B", "Option C", "Option D"],
-    "answer": "Option D"
-  }},
-  ...
-]
-
-TEXT:
-{text}
-"""
-
-prompt = PromptTemplate(
-    input_variables=["text", "number"],
-    template=template
-)
-
-mcq_chain = LLMChain(llm=llm, prompt=prompt)
-
-# PDF text extractor
-def extract_text_from_pdf(pdf):
-    doc = fitz.open(stream=pdf.read(), filetype="pdf")
-    full_text = ""
-    for page in doc:
-        full_text += page.get_text()
-    doc.close()
-    return full_text
-
-# Generate MCQs
-if st.sidebar.button("Generate MCQs"):
-    if pdf_file is None:
-        st.error("Please upload a PDF file.")
-    else:
-        with st.spinner("Extracting text and generating MCQs..."):
-            text = extract_text_from_pdf(pdf_file)
-            try:
-                response = mcq_chain.run(text=text, number=str(number_of_questions))
-                # st.subheader("🔍 Raw Output (Debugging)")
-                # st.code(response)
-                mcqs_json = json.loads(response.strip()[8:-3])
-                st.session_state.mcqs = mcqs_json
-                st.session_state.current_q = 0
-                st.session_state.user_answers = {}
-                st.session_state.quiz_finished = False
-                st.success("✅ MCQs generated successfully!")
-            except Exception as e:
-                st.error(f"Error generating MCQs: {e}")
-
-# Display question
-if st.session_state.mcqs and not st.session_state.quiz_finished:
-    idx = st.session_state.current_q
-    q_data = st.session_state.mcqs[idx]
-
-    st.subheader(f"Question {idx + 1}: {q_data['question']}")
-    selected_option = st.radio(
-        "Choose an answer:",
-        q_data["options"],
-        key=f"radio_{idx}"
-    )
-
-    if st.button("Next"):
-        st.session_state.user_answers[idx] = selected_option
-
-        if st.session_state.current_q < len(st.session_state.mcqs) - 1:
-            st.session_state.current_q += 1
-        else:
-            st.session_state.quiz_finished = True
-            st.success("🎉 Quiz completed!")
-
-# Show result
-if st.session_state.quiz_finished:
-    st.header("📊 Quiz Results")
-    score = 0
-    total = len(st.session_state.mcqs)
-
-    for i, q in enumerate(st.session_state.mcqs):
-        user_ans = st.session_state.user_answers.get(i)
-        correct_ans = q["answer"]
-        if user_ans == correct_ans:
-            score += 1
-        st.markdown(f"**Q{i+1}: {q['question']}**")
-        st.markdown(f"- Your answer: {user_ans}")
-        st.markdown(f"- Correct answer: {correct_ans}")
-        st.markdown("---")
-
-    st.success(f"✅ You scored {score} out of {total}")
+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_google_genai import ChatGoogleGenerativeAI
+import fitz
+import json
+
+# Title
+st.title("📄 PDF-based MCQ Generator")
+
+# Sidebar
+st.sidebar.title("Upload & Settings")
+
+# Upload PDF
+pdf_file = st.sidebar.file_uploader("Upload a PDF file", type=["pdf"])
+
+# Number of questions
+number_of_questions = st.sidebar.slider("Number of questions", min_value=1, max_value=20, value=5)
+
+# Session states
+if "mcqs" not in st.session_state:
+    st.session_state.mcqs = []
+if "current_q" not in st.session_state:
+    st.session_state.current_q = 0
+if "user_answers" not in st.session_state:
+    st.session_state.user_answers = {}
+if "quiz_finished" not in st.session_state:
+    st.session_state.quiz_finished = False
+
+# Gemini setup
+GOOGLE_API_KEY = "AIzaSyCB5NLx39vOAlfRQBDmnEG3uLBgLraGvH4"
+llm = ChatGoogleGenerativeAI(
+    model="gemini-2.0-flash",
+    google_api_key=GOOGLE_API_KEY,
+    temperature=0.7
+)
+
+template = """
+You are an expert MCQ generator. Generate {number} unique multiple-choice questions from the given text.
+Each question must have exactly 1 correct answer and 3 incorrect options.
+Strictly return output in the following JSON format (no explanations, no markdown):
+
+[
+  {{
+    "question": "What is ...?",
+    "options": ["Option A", "Option B", "Option C", "Option D"],
+    "answer": "Option D"
+  }},
+  ...
+]
+
+TEXT:
+{text}
+"""
+
+prompt = PromptTemplate(
+    input_variables=["text", "number"],
+    template=template
+)
+
+mcq_chain = LLMChain(llm=llm, prompt=prompt)
+
+# PDF text extractor
+def extract_text_from_pdf(pdf):
+    doc = fitz.open(stream=pdf.read(), filetype="pdf")
+    full_text = ""
+    for page in doc:
+        full_text += page.get_text()
+    doc.close()
+    return full_text
+
+# Generate MCQs
+if st.sidebar.button("Generate MCQs"):
+    if pdf_file is None:
+        st.error("Please upload a PDF file.")
+    else:
+        with st.spinner("Extracting text and generating MCQs..."):
+            text = extract_text_from_pdf(pdf_file)
+            try:
+                response = mcq_chain.run(text=text, number=str(number_of_questions))
+                # st.subheader("🔍 Raw Output (Debugging)")
+                # st.code(response)
+                mcqs_json = json.loads(response.strip())
+                st.session_state.mcqs = mcqs_json
+                st.session_state.current_q = 0
+                st.session_state.user_answers = {}
+                st.session_state.quiz_finished = False
+                st.success("✅ MCQs generated successfully!")
+            except Exception as e:
+                st.error(f"Error generating MCQs: {e}")
+
+# Display question
+if st.session_state.mcqs and not st.session_state.quiz_finished:
+    idx = st.session_state.current_q
+    q_data = st.session_state.mcqs[idx]
+
+    st.subheader(f"Question {idx + 1}: {q_data['question']}")
+    selected_option = st.radio(
+        "Choose an answer:",
+        q_data["options"],
+        key=f"radio_{idx}"
+    )
+
+    if st.button("Next"):
+        st.session_state.user_answers[idx] = selected_option
+
+        if st.session_state.current_q < len(st.session_state.mcqs) - 1:
+            st.session_state.current_q += 1
+        else:
+            st.session_state.quiz_finished = True
+            st.success("🎉 Quiz completed!")
+
+# Show result
+if st.session_state.quiz_finished:
+    st.header("📊 Quiz Results")
+    score = 0
+    total = len(st.session_state.mcqs)
+
+    for i, q in enumerate(st.session_state.mcqs):
+        user_ans = st.session_state.user_answers.get(i)
+        correct_ans = q["answer"]
+        if user_ans == correct_ans:
+            score += 1
+        st.markdown(f"**Q{i+1}: {q['question']}**")
+        st.markdown(f"- Your answer: {user_ans}")
+        st.markdown(f"- Correct answer: {correct_ans}")
+        st.markdown("---")
+
+    st.success(f"✅ You scored {score} out of {total}")