Update app.py

app.py

@@ -1,252 +1,78 @@
-import gradio as gr
 import os
-import io
-from google import generativeai as genai
-
-def process_exam_papers(question_paper, marking_scheme, answer_sheet, api_key, progress=gr.Progress()):
-    """
-    Process uploaded exam papers and return transcription and grading
-    """
-    if not api_key:
-        return "Please provide a valid Gemini API key.", "", "Ready"
-    
-    if not all([question_paper, marking_scheme, answer_sheet]):
-        return "Please upload all three files.", "", "Ready"
-    
-    try:
-        # Configure Gemini API
-        genai.configure(api_key=api_key)
-        
-        progress(0.1, desc="Uploading files to Gemini...")
-        
-        # Upload files to Gemini
-        qp_file = genai.upload_file(path=question_paper.name, display_name="Question Paper")
-        ms_file = genai.upload_file(path=marking_scheme.name, display_name="Marking Scheme")
-        ans_file = genai.upload_file(path=answer_sheet.name, display_name="Answer Sheet")
-        
-        progress(0.3, desc="Files uploaded. Starting transcription...")
-        
-        # Transcription instructions
-        transcription_instructions = """
-Persona:
-You are an expert transcriptionist specializing in scientific and mathematical documents. Your primary goal is to convert handwritten mathematical work into a perfectly formatted, machine-readable Markdown document using LaTeX for all mathematical notation.
-
-Core Task:
-Your task is to transcribe the provided handwritten student solutions into a single, clean Markdown string.
-
-Key Directives & Rules:
-Absolute Fidelity: Transcribe exactly what is written. Do NOT correct mathematical errors, logical fallacies, or spelling mistakes. Your role is purely that of a scribe, not a grader or editor.
+import gradio as gr
+import google.generativeai as genai
 
-LaTeX for All Math: All mathematical content—including single variables, numbers in equations, fractions, exponents, roots, and symbols—must be enclosed in LaTeX delimiters. Use inline $ ... $ for math within text and block $$ ... $$ for standalone equations.
+# ✅ Configure Gemini with Hugging Face Secret
+genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
 
-Handle Strikethroughs: Completely ignore and omit any text, numbers, or expressions that have been struck through by the student. Do not include them in the final output.
+# ---------- PROMPTS ----------
+TRANSCRIPTION_PROMPT = """
+Persona:
+You are an expert transcriptionist specializing in scientific and mathematical documents. 
+Your primary goal is to convert handwritten mathematical work into a perfectly formatted, 
+machine-readable Markdown document using LaTeX for all mathematical notation.
 
-Preserve Structure:
-Use Markdown bolding (e.g., **1.**, **2a.**) to clearly separate each question or sub-part.
-Maintain the vertical, step-by-step flow of the student's derivations. For multi-line aligned equations, use the \\begin{align*} ... \\end{align*} environment within a $$ ... $$ block.
+Rules:
+- Transcribe exactly what is written, do not correct errors.
+- Use $...$ for inline math, $$...$$ for block math.
+- Ignore struck-through text.
+- Preserve structure: bold for Q numbers (**1.**), step-by-step math with \\begin{align*}.
+- If a symbol is ambiguous, mark as [x?].
+Output must be a clean Markdown string.
+"""
 
-Handle Ambiguity: If a character or symbol is genuinely illegible or ambiguous, make your best interpretation and enclose it in square brackets. For example, if a variable could be u or v, write [u?].
+GRADING_PROMPT = """
+You are an official examiner. Grade the student transcription using the question paper 
+and the official marking scheme.
 
-Output Format:
-The final output must be a single Markdown string.
-Ensure all LaTeX renders correctly and the structure is clean and readable.
+Rules:
+1. Apply marks exactly as per the markscheme (M1, A1, etc.).
+2. M marks must be earned before A marks.
+3. Justify each awarded or withheld mark with clear reasoning.
+4. Classify all errors as Conceptual Error, Silly Mistake, or None.
+5. Follow dependency between M and A strictly.
+6. Do not give marks outside the markscheme.
+Output must be a structured grading report with reasoning.
+"""
 
-Comprehensive Example:
-If the student's handwritten work for a question looks like this:
-7. Find the value of y.
-y = (x² + 3) / 2
-for x = 3
-y = (3² + 3) / 2
-y = (6+3) / 2
-y = (9 + 3) / 2
-y = 12 / 2
-y = 6
+# ---------- CORE FUNCTION ----------
+def grade_student(qp_file, ms_file, ans_file):
+    try:
+        # Upload files to Gemini
+        qp_uploaded = genai.upload_file(path=qp_file.name, display_name="Question Paper")
+        ms_uploaded = genai.upload_file(path=ms_file.name, display_name="Marking Scheme")
+        ans_uploaded = genai.upload_file(path=ans_file.name, display_name="Answer Sheet")
 
-Your expected output should be:
-**7.**
+        # Step 1: Transcription
+        model = genai.GenerativeModel("gemini-2.5-pro", generation_config={"temperature": 0})
+        transcription_resp = model.generate_content([TRANSCRIPTION_PROMPT, ans_uploaded])
+        transcription = getattr(transcription_resp, "text", None) or transcription_resp.candidates[0].content.parts[0].text
 
-Find the value of y.
-$$
-y = \\frac{x^2 + 3}{2}
-$$
-for $x = 3$
-$$
-\\begin{align*}
-y &= \\frac{3^2 + 3}{2} \\\\
-y &= \\frac{9 + 3}{2} \\\\
-y &= \\frac{12}{2} \\\\
-y &= 6
-\\end{align*}
-$$
-"""
-        
-        # Initialize Gemini model for transcription
-        model = genai.GenerativeModel(
-            "gemini-2.5-pro",
-            generation_config={"temperature": 0}
-        )
-        
-        progress(0.4, desc="Transcribing handwritten answers...")
-        
-        # Generate transcription
-        response = model.generate_content([
-            transcription_instructions,
-            ans_file
-        ])
-        
-        # Extract transcription safely
-        student_transcription = getattr(response, "text", None)
-        if not student_transcription:
-            student_transcription = response.candidates[0].content.parts[0].text
-        
-        progress(0.7, desc="Transcription complete. Starting grading process...")
-        
-        # Return transcription first, then continue with grading
-        yield student_transcription, "⏳ Grading in progress...", "Grading"
-        
-        # Grading system instructions
-        grading_system = """
-Instructions to Examiners:
-Abbreviations:
-- M: Marks for correct Method.
-- A: Marks for Answer or Accuracy (often depends on preceding M mark).
-- R: Marks for clear Reasoning.
-- AG: Answer given in the question; no marks awarded.
-- FT: Follow Through; award marks for correct method/answer using incorrect earlier results.
+        # Step 2: Grading
+        grading_resp = model.generate_content([GRADING_PROMPT, qp_uploaded, ms_uploaded, transcription])
+        grading = getattr(grading_resp, "text", None) or grading_resp.candidates[0].content.parts[0].text
 
-Marking Rules:
-1. Always follow the markscheme annotations (M1, A2, etc.).
-2. M marks must be earned before dependent A marks are awarded (no M0 followed by A1 unless explicitly allowed).
-3. If M and A marks are on the same line (e.g., M1A1), M is for the method attempt, A is for correct values.
-4. Multiple A marks on the same line are awarded independently unless otherwise noted.
-5. Do not split M2, A3, etc. unless instructed.
-6. "Show that" responses do not need to restate the AG line unless noted.
-7. Once a correct answer is seen, ignore further incorrect working unless it affects a later part (then apply FT as appropriate).
-8. Do not award the final A mark if an incorrect approximation is used in the same part.
+        return transcription, grading
 
-Error Avoidance:
-- **No incorrect mark allocation:** Do not award marks unless they are explicitly justified by the markscheme.
-- **No misclassification of errors:** Distinguish correctly between "Conceptual Errors" and "Silly Mistakes."
-- **Follow markscheme logic exactly:** Especially regarding when to withhold accuracy marks if method marks are not earned.
-"""
-        
-        # Now start grading using the transcribed text
-        # Generate grading
-        grading_response = model.generate_content([
-            f"You are an official examiner. Use the following grading system and rules to assess the answers:\n\n{grading_system}\n\n"
-            "Your output must:\n"
-            "1. Apply marks exactly as per the markscheme.\n"
-            "2. Justify each awarded or withheld mark with reference to the grading rules.\n"
-            "3. Identify and classify all errors accurately (Conceptual Error, Silly Mistake, or None).\n"
-            "4. Follow the dependency between M and A marks strictly.\n"
-            "5. Avoid giving marks that the markscheme does not allow.\n"
-            "6. Provide a step-by-step reasoning for each mark awarded or withheld, explaining your thought process clearly.\n",
-            qp_file,
-            ms_file,
-            student_transcription  # Use the transcribed text, not the original PDF
-        ])
-        
-        progress(0.9, desc="Finalizing grading results...")
-        
-        # Extract grading safely
-        grading_text = getattr(grading_response, "text", None)
-        if not grading_text and grading_response.candidates:
-            grading_text = grading_response.candidates[0].content.parts[0].text
-        elif not grading_text:
-            grading_text = "No Response"
-        
-        progress(1.0, desc="Complete!")
-        
-        # Return final results
-        yield student_transcription, grading_text, "Complete"
-        
     except Exception as e:
-        yield f"Error processing files: {str(e)}", "", "Error"
+        return "❌ Error during processing: " + str(e), ""
+
 
-# Create Gradio interface
-with gr.Blocks(title="Exam Paper Grading System", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 📚 Automated Exam Paper Grading System
-    
-    Upload your question paper, marking scheme, and answer sheet to get automated transcription and grading using Google's Gemini AI.
-    """)
-    
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("### 📋 Upload Files")
-            api_key = gr.Textbox(
-                label="Gemini API Key",
-                placeholder="Enter your Google Gemini API key",
-                type="password"
-            )
-            question_paper = gr.File(
-                label="Question Paper (PDF)",
-                file_types=[".pdf"]
-            )
-            marking_scheme = gr.File(
-                label="Marking Scheme (PDF)",
-                file_types=[".pdf"]
-            )
-            answer_sheet = gr.File(
-                label="Answer Sheet (PDF)",
-                file_types=[".pdf"]
-            )
-            
-            process_btn = gr.Button(
-                "🚀 Process Papers",
-                variant="primary",
-                size="lg"
-            )
-    
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("### 📝 Student Answer Transcription")
-            transcription_output = gr.Textbox(
-                label="Transcribed Answers",
-                lines=15,
-                max_lines=25,
-                show_copy_button=True,
-                placeholder="Transcribed answers will appear here first..."
-            )
-        
-        with gr.Column():
-            gr.Markdown("### ✅ Grading Results")
-            grading_output = gr.Textbox(
-                label="Detailed Grading",
-                lines=15,
-                max_lines=25,
-                show_copy_button=True,
-                placeholder="Grading results will appear here after transcription is complete..."
-            )
-    
-    # Add status indicator
-    with gr.Row():
-        status_display = gr.Textbox(
-            label="Status",
-            value="Ready",
-            interactive=False,
-            show_label=True
-        )
-    
-    # Set up the processing function
-    process_btn.click(
-        fn=process_exam_papers,
-        inputs=[question_paper, marking_scheme, answer_sheet, api_key],
-        outputs=[transcription_output, grading_output, status_display]
-    )
-    
-    gr.Markdown("""
-    ### 📖 How to Use:
-    1. **Get a Gemini API Key**: Visit [Google AI Studio](https://makersuite.google.com/app/apikey) to get your free API key
-    2. **Upload PDFs**: Upload your question paper, marking scheme, and student answer sheet
-    3. **Process**: Click the "Process Papers" button to get transcription and grading
-    4. **Review**: Check the transcribed answers and detailed grading results
-    
-    ### ⚠️ Notes:
-    - All uploaded files are processed securely and not stored permanently
-    - The system transcribes exactly what's written (including errors) for accurate grading
-    - LaTeX mathematical notation is automatically formatted for clarity
-    """)
+# ---------- GRADIO UI ----------
+demo = gr.Interface(
+    fn=grade_student,
+    inputs=[
+        gr.File(label="Upload Question Paper (PDF)", type="file"),
+        gr.File(label="Upload Mark Scheme (PDF)", type="file"),
+        gr.File(label="Upload Student Answer Sheet (PDF)", type="file")
+    ],
+    outputs=[
+        gr.Markdown(label="Student Transcription"),
+        gr.Textbox(label="Grading Report")
+    ],
+    title="📘 AI Teacher Assistant",
+    description="Upload a Question Paper, Mark Scheme, and Student Answer Sheet. The app transcribes handwritten answers into Markdown/LaTeX and grades them using the official scheme."
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()