initial commit

app.py

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+import streamlit as st
+from groq_client import (
+    extract_file_id,
+    download_public_notebook,
+    review_notebook_content,
+)
+
+st.set_page_config(page_title="Notebook Reviewer", layout="wide")
+
+# Page title and description
+st.title("📓 Jupyter Notebook Reviewer with Groq AI")
+st.markdown(
+    """
+    Upload a Jupyter Notebook (`.ipynb`) file or provide a Google Drive link to receive **detailed feedback** on each code cell.  
+    Each cell's context, code, and output are analyzed for accuracy, readability, and efficiency.
+    """
+)
+
+# Layout: Sidebar for input options
+with st.sidebar:
+    st.header("📥 Upload or Provide Link")
+    uploaded_file = st.file_uploader("Upload Notebook File", type=["ipynb"])
+    google_drive_link = st.text_input("OR Enter Google Drive Link")
+
+# Main review section
+st.subheader("📑 Notebook Review")
+
+if st.button("Review Notebook"):
+    notebook_content = None
+
+    # Process notebook input
+    if uploaded_file:
+        notebook_content = uploaded_file.getvalue().decode("utf-8")
+        st.success("Notebook uploaded successfully!")
+    elif google_drive_link:
+        try:
+            file_id = extract_file_id(google_drive_link)
+            notebook_content = download_public_notebook(file_id)
+            st.success("Notebook downloaded successfully from Google Drive!")
+        except Exception as e:
+            st.error(f"Error: {e}")
+            st.stop()
+    else:
+        st.error("Please upload a file or provide a Google Drive link.")
+        st.stop()
+
+    # Analyze the notebook content
+    try:
+        feedback = review_notebook_content(notebook_content)
+        st.success(
+            "Notebook successfully reviewed! Scroll down to see detailed feedback."
+        )
+
+        # Display feedback for each cell
+        for cell_index, cell_feedback in enumerate(feedback, start=1):
+            with st.expander(f"Feedback for Cell {cell_index}", expanded=False):
+                # Splitting feedback into sections
+                cell_lines = cell_feedback.split("\n")
+                markdown_lines = []
+                code_block = []
+
+                for line in cell_lines:
+                    if line.strip().startswith("```"):  # Identify code block markers
+                        code_block.append(line.strip("`"))
+                    elif code_block:
+                        code_block.append(line)
+                    else:
+                        markdown_lines.append(line)
+
+                # Render text feedback
+                st.markdown("\n".join(markdown_lines))
+
+                # Render code if present
+                if code_block:
+                    st.code("\n".join(code_block), language="python")
+
+    except Exception as e:
+        st.error(f"An error occurred: {e}")

groq_client.py

@@ -0,0 +1,115 @@
+from groq import Groq
+import requests
+import nbformat
+
+# Initialize the Groq client
+GROQ_API_KEY = "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"
+client = Groq(api_key=GROQ_API_KEY)
+
+
+def extract_file_id(google_drive_link):
+    if "id=" in google_drive_link:
+        return google_drive_link.split("id=")[-1]
+    elif "drive.google.com/file/d/" in google_drive_link:
+        return google_drive_link.split("file/d/")[-1].split("/")[0]
+    else:
+        raise ValueError("Invalid Google Drive link.")
+
+
+def download_public_notebook(file_id):
+    download_url = f"https://drive.google.com/uc?export=download&id={file_id}"
+    response = requests.get(download_url)
+    response.raise_for_status()
+    return response.text
+
+
+def parse_notebook_with_markdown(notebook_content):
+    """
+    Parse the notebook to extract markdown, code, and corresponding outputs.
+    Markdown cells preceding a code cell are associated with it.
+    """
+    notebook = nbformat.reads(notebook_content, as_version=4)
+    cells = notebook.cells
+
+    parsed_cells = []
+    current_markdown = ""
+
+    for cell in cells:
+        if cell.cell_type == "markdown":
+            current_markdown += cell.source + "\n\n"  # Append markdown content
+        elif cell.cell_type == "code":
+            code = cell.source
+            output = (
+                "".join(
+                    output.text for output in cell.outputs if hasattr(output, "text")
+                )
+                or "No output"
+            )
+            parsed_cells.append((current_markdown.strip(), code, output))
+            current_markdown = (
+                ""  # Reset markdown context after associating it with a code cell
+            )
+
+    return parsed_cells
+
+
+def send_to_groq_api_with_context(markdown, code, output, cell_index):
+    """
+    Sends markdown, code, and output to the Groq API for grading.
+    """
+    prompt = f"""
+    You are a Python notebook grader. Below is a question or context from a Jupyter notebook, followed by the corresponding Python code and its output.
+
+    Please provide:
+    1. A grade out of 10 for this cell.
+    2. Detailed remarks on the quality of the code, including its readability, efficiency, and accuracy.
+    3. Suggestions for improving the code if necessary.
+    4. Comments on how well the code addresses the context or question provided.
+    5. Actionable suggestions for improvement, including alternative solutions and learning resources.
+    6. Code Quality: Assess readability, modularity, and adherence to Pythonic practices.
+    7. Efficiency: Evaluate computational efficiency and suggest optimizations if necessary.
+    8. Outputs: Verify the correctness and clarity of the outputs.
+    9. Areas for improvement: Mention specific areas where the code or logic can be improved.
+    10. Logical Errors: Specifically check if the code has logical errors or flaws that would prevent it from achieving the intended result, even if it runs without errors.
+    
+    Cell Index: {cell_index}
+
+    Context or Question:
+    {markdown if markdown else "No specific context provided."}
+
+    Code:
+    {code}
+
+    Output:
+    {output}
+    """
+
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a Python notebook grader focusing on the provided context, code, and output for each cell. Provide a detailed analysis based on the following criteria, including checking for any logical errors that might affect the correctness of the code.",
+            },
+            {"role": "user", "content": prompt},
+        ],
+        model="llama3-8b-8192",
+        temperature=0.5,
+        max_tokens=1500,
+        top_p=1,
+    )
+
+    return chat_completion.choices[0].message.content
+
+
+def review_notebook_content(notebook_content):
+    """
+    Reviews a notebook's content and returns detailed feedback for each cell.
+    """
+    parsed_cells = parse_notebook_with_markdown(notebook_content)
+    feedback_list = []
+
+    for i, (markdown, code, output) in enumerate(parsed_cells, start=1):
+        feedback = send_to_groq_api_with_context(markdown, code, output, i)
+        feedback_list.append(f"Feedback for Cell {i}:\n{feedback}\n{'-' * 50}")
+
+    return feedback_list