Update app.py

app.py

@@ -1,263 +1,263 @@
-import streamlit as st
-from langchain_groq import ChatGroq
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate
-from dotenv import load_dotenv
-import os
-import pytesseract
-from PIL import Image
-import pdfplumber
-import docx
-from io import BytesIO
-from sentence_transformers import SentenceTransformer
-from pinecone import Pinecone, ServerlessSpec
-import logging
-
-# Load environment variables
-load_dotenv()
-
-# Initialize logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-# Initialize LLM
-llm = ChatGroq(temperature=0.5, groq_api_key="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", model_name="llama3-8b-8192")
-
-# Initialize Pinecone for vector storage
-PINECONE_API_KEY = "pcsk_6PtxDh_6tortuWyNhXdmVrAjx1ZSv8bQRcbgbE7j3JtwwcpMCkFfdsp6VC925WxmqpNYQC"
-pc = Pinecone(api_key=PINECONE_API_KEY)
-
-cloud = os.getenv('PINECONE_CLOUD', 'aws')
-region = os.getenv('PINECONE_REGION', 'us-east-1')
-
-spec = ServerlessSpec(cloud=cloud, region=region)
-
-index_name = "syllabus-index"
-if index_name not in pc.list_indexes().names():
-    pc.create_index(
-        name=index_name,
-        dimension=384,
-        spec=spec
-    )
-
-index = pc.Index(index_name)
-
-# Initialize embedding model
-embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-
-# OCR Configuration for Pytesseract
-pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract'  # Adjust to your system's path
-
-# Function to extract text, images, tables, and formulas from PDF
-def extract_pdf_data(pdf_path):
-    data = {"text": "", "tables": [], "images": []}
-    try:
-        with pdfplumber.open(pdf_path) as pdf:
-            for page in pdf.pages:
-                # Extract Text
-                data["text"] += page.extract_text() or ""
-                # Extract Tables
-                tables = page.extract_tables()
-                for table in tables:
-                    data["tables"].append(table)
-                # Extract Images
-                for image in page.images:
-                    base_image = pdf.extract_image(image["object_number"])
-                    image_obj = Image.open(BytesIO(base_image["image"]))
-                    data["images"].append(image_obj)
-    except Exception as e:
-        logging.error(f"Error processing PDF: {e}")
-    return data
-
-# Function to extract text from DOCX files
-def extract_docx_data(docx_file):
-    doc = docx.Document(docx_file)
-    text = ""
-    for para in doc.paragraphs:
-        text += para.text + "\n"
-    return text
-
-# Function to extract text from plain text files
-def extract_text_file_data(text_file):
-    return text_file.read().decode('utf-8')
-
-# Function to extract text from images using OCR
-def extract_text_from_images(images):
-    ocr_text = ""
-    for image in images:
-        ocr_text += pytesseract.image_to_string(image) + "\n"
-    return ocr_text
-
-# Function to process extracted content (PDF, DOCX, etc.)
-def process_content(file_data, file_type="pdf"):
-    text = ""
-    images = []
-    if file_type == "pdf":
-        pdf_data = extract_pdf_data(file_data)
-        text = process_pdf_content(pdf_data)
-        images = pdf_data["images"]
-    elif file_type == "docx":
-        text = extract_docx_data(file_data)
-    elif file_type == "txt":
-        text = extract_text_file_data(file_data)
-
-    ocr_text = extract_text_from_images(images)
-    return text + "\n" + ocr_text
-
-# Function to process PDF content
-def process_pdf_content(pdf_data):
-    # Process OCR text from images
-    ocr_text = extract_text_from_images(pdf_data["images"])
-    combined_text = pdf_data["text"] + ocr_text
-
-    # Process tables into readable text
-    table_text = ""
-    for table in pdf_data["tables"]:
-        table_rows = [" | ".join(row) for row in table]
-        table_text += "\n".join(table_rows) + "\n"
-
-    return combined_text + "\n" + table_text
-
-# Function to add syllabus to vector database
-def add_syllabus_to_index(syllabus_text):
-    sentences = syllabus_text.split(". ")
-    embeddings = embedder.encode(sentences, batch_size=32, show_progress_bar=True)
-    for i, sentence in enumerate(sentences):
-        index.upsert([(f"sentence-{i}", embeddings[i].tolist(), {"text": sentence})])
-
-# Function to retrieve relevant syllabus content
-def retrieve_relevant_content(query):
-    try:
-        query_embedding = embedder.encode([query])
-        results = index.query(vector=query_embedding.tolist(), top_k=5, include_metadata=True)
-        relevant_content = "\n".join([match["metadata"]["text"] for match in results["matches"]])
-        return relevant_content
-    except Exception as e:
-        logging.error(f"Error retrieving content: {e}")
-        return ""
-
-# Function to generate questions
-def generate_questions(question_type, subject_name, syllabus_context, num_questions, difficulty_level):
-    prompt_template = f"""
-    Based on the following syllabus content, generate {num_questions} {question_type} questions. Ensure the questions are directly derived from the provided syllabus content.
-
-    Subject: {subject_name}
-    Syllabus Content: {syllabus_context}
-
-    Difficulty Levels:
-    - Remember: {difficulty_level.get('Remember', 0)}
-    - Understand: {difficulty_level.get('Understand', 0)}
-    - Apply: {difficulty_level.get('Apply', 0)}
-    - Analyze: {difficulty_level.get('Analyze', 0)}
-    - Evaluate: {difficulty_level.get('Evaluate', 0)}
-    - Create: {difficulty_level.get('Create', 0)}
-
-    Format questions as follows:
-    Q1. ________________
-
-    Q2. ________________
-
-    ...
-    """
-    chain = (ChatPromptTemplate.from_template(prompt_template) | llm | StrOutputParser())
-    try:
-        return chain.invoke({})
-    except Exception as e:
-        logging.error(f"Error generating {question_type} questions: {e}")
-        return ""
-
-# Function to generate answers
-def generate_answers(questions, syllabus_context):
-    prompt = f"""
-    Based on the provided syllabus content, generate detailed answers for the following questions. The answers must only be based on the syllabus content.
-
-    Syllabus Content: {syllabus_context}
-
-    Questions:
-    {questions}
-
-    Format answers as follows:
-    Answer 1: ________________
-    Answer 2: ________________
-    ...
-    """
-    chain = (ChatPromptTemplate.from_template(prompt) | llm | StrOutputParser())
-    try:
-        return chain.invoke({})
-    except Exception as e:
-        logging.error(f"Error generating answers: {e}")
-        return ""
-
-# Streamlit app
-st.title("Bloom Taxonomy Based Exam Paper Developer")
-
-# Sidebar inputs
-instructor_name = st.sidebar.text_input("Instructor")
-class_name = st.sidebar.text_input("Class")
-institution_name = st.sidebar.text_input("Institution")
-subject_name = st.sidebar.text_input("Subject")
-
-# Syllabus Upload
-uploaded_file = st.sidebar.file_uploader("Upload Syllabus (PDF, DOCX, TXT, Image)", type=["pdf", "docx", "txt", "png", "jpg"])
-syllabus_text = None
-if uploaded_file:
-    file_type = uploaded_file.type.split("/")[1]
-    st.sidebar.markdown("✅ Syllabus uploaded")
-    syllabus_text = process_content(uploaded_file, file_type)
-    add_syllabus_to_index(syllabus_text)
-
-# Preview of Syllabus
-if syllabus_text:
-    st.subheader("Syllabus Preview:")
-    st.text_area("Extracted Content", syllabus_text[:1000], height=300)
-
-# Question Type Selection
-question_type = st.sidebar.radio("Select Question Type", ("MCQs", "Short Questions", "Long Questions", "Fill in the Blanks", "Case Studies", "Diagram-based"))
-difficulty_levels = ["Remember", "Understand", "Apply", "Analyze", "Evaluate", "Create"]
-difficulty = {level: st.sidebar.slider(level, 0, 5, 1) for level in difficulty_levels}
-num_questions = st.sidebar.number_input("Number of Questions", min_value=1, max_value=50, value=10)
-
-# Instructor Feedback Option
-feedback = st.sidebar.text_area("Instructor Feedback (Optional)")
-
-# Generate Questions
-if st.sidebar.button("Generate Questions"):
-    if syllabus_text:
-        with st.spinner(f"Generating {question_type}..."):
-            syllabus_context = retrieve_relevant_content(f"Generate {question_type} based on syllabus")
-            st.session_state.generated_questions = generate_questions(question_type, subject_name, syllabus_context, num_questions, difficulty)
-        st.text_area(f"Generated {question_type}", value=st.session_state.generated_questions, height=400)
-    else:
-        st.error("Please upload a syllabus before generating questions.")
-
-# Generate Answers
-if st.sidebar.button("Generate Answers for Questions"):
-    if "generated_questions" in st.session_state and st.session_state.generated_questions:
-        with st.spinner("Generating answers..."):
-            syllabus_context = retrieve_relevant_content("Generate answers from syllabus")
-            st.session_state.generated_answers = generate_answers(st.session_state.generated_questions, syllabus_context)
-        st.text_area("Generated Answers", value=st.session_state.generated_answers, height=400)
-    else:
-        st.error("Generate questions first before generating answers.")
-
-# Download Options
-if "generated_questions" in st.session_state and st.session_state.generated_questions:
-    st.sidebar.download_button(
-        label="Download Questions",
-        data=st.session_state.generated_questions,
-        file_name=f"{subject_name}_questions.txt",
-        mime="text/plain",
-    )
-
-if "generated_answers" in st.session_state and st.session_state.generated_answers:
-    st.sidebar.download_button(
-        label="Download Answers",
-        data=st.session_state.generated_answers,
-        file_name=f"{subject_name}_answers.txt",
-        mime="text/plain",
-    )
-
-# Application Footer
-st.markdown("""
----
-**Advanced Test Paper Generator** - powered by LangChain, Pinecone, and Streamlit.
-""")
+import streamlit as st
+from langchain_groq import ChatGroq
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from dotenv import load_dotenv
+import os
+import pytesseract
+from PIL import Image
+import pdfplumber
+import docx
+from io import BytesIO
+from sentence_transformers import SentenceTransformer
+from pinecone import Pinecone, ServerlessSpec
+import logging
+
+# Load environment variables
+load_dotenv()
+
+# Initialize logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Initialize LLM
+llm = ChatGroq(temperature=0.5, groq_api_key="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", model_name="llama3-8b-8192")
+
+# Initialize Pinecone for vector storage
+PINECONE_API_KEY = "pcsk_6PtxDh_6tortuWyNhXdmVrAjx1ZSv8bQRcbgbE7j3JtwwcpMCkFfdsp6VC925WxmqpNYQC"
+pc = Pinecone(api_key=PINECONE_API_KEY)
+
+cloud = os.getenv('PINECONE_CLOUD', 'aws')
+region = os.getenv('PINECONE_REGION', 'us-east-1')
+
+spec = ServerlessSpec(cloud=cloud, region=region)
+
+index_name = "syllabus-index"
+if index_name not in pc.list_indexes().names():
+    pc.create_index(
+        name=index_name,
+        dimension=384,
+        spec=spec
+    )
+
+index = pc.Index(index_name)
+
+# Initialize embedding model
+embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# OCR Configuration for Pytesseract
+pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract'  # Adjust to your system's path
+
+# Function to extract text, images, tables, and formulas from PDF
+def extract_pdf_data(pdf_path):
+    data = {"text": "", "tables": [], "images": []}
+    try:
+        with pdfplumber.open(pdf_path) as pdf:
+            for page in pdf.pages:
+                # Extract Text
+                data["text"] += page.extract_text() or ""
+                # Extract Tables
+                tables = page.extract_tables()
+                for table in tables:
+                    data["tables"].append(table)
+                # Extract Images
+                for image in page.images:
+                    base_image = pdf.extract_image(image["object_number"])
+                    image_obj = Image.open(BytesIO(base_image["image"]))
+                    data["images"].append(image_obj)
+    except Exception as e:
+        logging.error(f"Error processing PDF: {e}")
+    return data
+
+# Function to extract text from DOCX files
+def extract_docx_data(docx_file):
+    doc = docx.Document(docx_file)
+    text = ""
+    for para in doc.paragraphs:
+        text += para.text + "\n"
+    return text
+
+# Function to extract text from plain text files
+def extract_text_file_data(text_file):
+    return text_file.read().decode('utf-8')
+
+# Function to extract text from images using OCR
+def extract_text_from_images(images):
+    ocr_text = ""
+    for image in images:
+        ocr_text += pytesseract.image_to_string(image) + "\n"
+    return ocr_text
+
+# Function to process extracted content (PDF, DOCX, etc.)
+def process_content(file_data, file_type="pdf"):
+    text = ""
+    images = []
+    if file_type == "pdf":
+        pdf_data = extract_pdf_data(file_data)
+        text = process_pdf_content(pdf_data)
+        images = pdf_data["images"]
+    elif file_type == "docx":
+        text = extract_docx_data(file_data)
+    elif file_type == "txt":
+        text = extract_text_file_data(file_data)
+
+    ocr_text = extract_text_from_images(images)
+    return text + "\n" + ocr_text
+
+# Function to process PDF content
+def process_pdf_content(pdf_data):
+    # Process OCR text from images
+    ocr_text = extract_text_from_images(pdf_data["images"])
+    combined_text = pdf_data["text"] + ocr_text
+
+    # Process tables into readable text
+    table_text = ""
+    for table in pdf_data["tables"]:
+        table_rows = [" | ".join(row) for row in table]
+        table_text += "\n".join(table_rows) + "\n"
+
+    return combined_text + "\n" + table_text
+
+# Function to add syllabus to vector database
+def add_syllabus_to_index(syllabus_text):
+    sentences = syllabus_text.split(". ")
+    embeddings = embedder.encode(sentences, batch_size=32, show_progress_bar=True)
+    for i, sentence in enumerate(sentences):
+        index.upsert([(f"sentence-{i}", embeddings[i].tolist(), {"text": sentence})])
+
+# Function to retrieve relevant syllabus content
+def retrieve_relevant_content(query):
+    try:
+        query_embedding = embedder.encode([query])
+        results = index.query(vector=query_embedding.tolist(), top_k=5, include_metadata=True)
+        relevant_content = "\n".join([match["metadata"]["text"] for match in results["matches"]])
+        return relevant_content
+    except Exception as e:
+        logging.error(f"Error retrieving content: {e}")
+        return ""
+
+# Function to generate questions
+def generate_questions(question_type, subject_name, syllabus_context, num_questions, difficulty_level):
+    prompt_template = f"""
+    Based on the following syllabus content, generate {num_questions} {question_type} questions. Ensure the questions are directly derived from the provided syllabus content.
+
+    Subject: {subject_name}
+    Syllabus Content: {syllabus_context}
+
+    Difficulty Levels:
+    - Remember: {difficulty_level.get('Remember', 0)}
+    - Understand: {difficulty_level.get('Understand', 0)}
+    - Apply: {difficulty_level.get('Apply', 0)}
+    - Analyze: {difficulty_level.get('Analyze', 0)}
+    - Evaluate: {difficulty_level.get('Evaluate', 0)}
+    - Create: {difficulty_level.get('Create', 0)}
+
+    Format questions as follows:
+    Q1. ________________
+
+    Q2. ________________
+
+    ...
+    """
+    chain = (ChatPromptTemplate.from_template(prompt_template) | llm | StrOutputParser())
+    try:
+        return chain.invoke({})
+    except Exception as e:
+        logging.error(f"Error generating {question_type} questions: {e}")
+        return ""
+
+# Function to generate answers
+def generate_answers(questions, syllabus_context):
+    prompt = f"""
+    Based on the provided syllabus content, generate detailed answers for the following questions. The answers must only be based on the syllabus content.
+
+    Syllabus Content: {syllabus_context}
+
+    Questions:
+    {questions}
+
+    Format answers as follows:
+    Answer 1: ________________
+    Answer 2: ________________
+    ...
+    """
+    chain = (ChatPromptTemplate.from_template(prompt) | llm | StrOutputParser())
+    try:
+        return chain.invoke({})
+    except Exception as e:
+        logging.error(f"Error generating answers: {e}")
+        return ""
+
+# Streamlit app
+st.title("Bloom's Taxonomy Based Exam Paper Developer")
+
+# Sidebar inputs
+instructor_name = st.sidebar.text_input("Instructor")
+class_name = st.sidebar.text_input("Class")
+institution_name = st.sidebar.text_input("Institution")
+subject_name = st.sidebar.text_input("Subject")
+
+# Syllabus Upload
+uploaded_file = st.sidebar.file_uploader("Upload Syllabus (PDF, DOCX, TXT, Image)", type=["pdf", "docx", "txt", "png", "jpg"])
+syllabus_text = None
+if uploaded_file:
+    file_type = uploaded_file.type.split("/")[1]
+    st.sidebar.markdown("✅ Syllabus uploaded")
+    syllabus_text = process_content(uploaded_file, file_type)
+    add_syllabus_to_index(syllabus_text)
+
+# Preview of Syllabus
+if syllabus_text:
+    st.subheader("Syllabus Preview:")
+    st.text_area("Extracted Content", syllabus_text[:1000], height=300)
+
+# Question Type Selection
+question_type = st.sidebar.radio("Select Question Type", ("MCQs", "Short Questions", "Long Questions", "Fill in the Blanks", "Case Studies", "Diagram-based"))
+difficulty_levels = ["Remember", "Understand", "Apply", "Analyze", "Evaluate", "Create"]
+difficulty = {level: st.sidebar.slider(level, 0, 5, 1) for level in difficulty_levels}
+num_questions = st.sidebar.number_input("Number of Questions", min_value=1, max_value=50, value=10)
+
+# Instructor Feedback Option
+feedback = st.sidebar.text_area("Instructor Feedback (Optional)")
+
+# Generate Questions
+if st.sidebar.button("Generate Questions"):
+    if syllabus_text:
+        with st.spinner(f"Generating {question_type}..."):
+            syllabus_context = retrieve_relevant_content(f"Generate {question_type} based on syllabus")
+            st.session_state.generated_questions = generate_questions(question_type, subject_name, syllabus_context, num_questions, difficulty)
+        st.text_area(f"Generated {question_type}", value=st.session_state.generated_questions, height=400)
+    else:
+        st.error("Please upload a syllabus before generating questions.")
+
+# Generate Answers
+if st.sidebar.button("Generate Answers for Questions"):
+    if "generated_questions" in st.session_state and st.session_state.generated_questions:
+        with st.spinner("Generating answers..."):
+            syllabus_context = retrieve_relevant_content("Generate answers from syllabus")
+            st.session_state.generated_answers = generate_answers(st.session_state.generated_questions, syllabus_context)
+        st.text_area("Generated Answers", value=st.session_state.generated_answers, height=400)
+    else:
+        st.error("Generate questions first before generating answers.")
+
+# Download Options
+if "generated_questions" in st.session_state and st.session_state.generated_questions:
+    st.sidebar.download_button(
+        label="Download Questions",
+        data=st.session_state.generated_questions,
+        file_name=f"{subject_name}_questions.txt",
+        mime="text/plain",
+    )
+
+if "generated_answers" in st.session_state and st.session_state.generated_answers:
+    st.sidebar.download_button(
+        label="Download Answers",
+        data=st.session_state.generated_answers,
+        file_name=f"{subject_name}_answers.txt",
+        mime="text/plain",
+    )
+
+# Application Footer
+st.markdown("""
+---
+**Advanced Test Paper Generator** - powered by LangChain, Pinecone, and Streamlit.
+""")