Create app.py

app.py

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+import os
+import gradio as gr
+import google.generativeai as genai
+
+# Configure Gemini API (key must be set in Hugging Face Space secrets)
+genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+
+# ---------- 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.
+
+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.
+"""
+
+GRADING_PROMPT = """
+You are an official examiner. Grade the student transcription using the question paper 
+and the official marking scheme.
+
+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.
+"""
+
+# ---------- STEP 1: TRANSCRIPTION ----------
+def transcribe(ans_file):
+    yield "⏳ Processing transcription..."
+    try:
+        ans_uploaded = genai.upload_file(path=ans_file.name, display_name="Answer Sheet")
+
+        model = genai.GenerativeModel("gemini-2.5-pro", generation_config={"temperature": 0})
+        resp = model.generate_content([TRANSCRIPTION_PROMPT, ans_uploaded])
+
+        transcription = getattr(resp, "text", None) or resp.candidates[0].content.parts[0].text
+
+        yield transcription  # Markdown will render LaTeX properly
+    except Exception as e:
+        yield f"❌ Error during transcription: {e}"
+
+# ---------- STEP 2: GRADING ----------
+def grade(qp_file, ms_file, transcription):
+    yield "⏳ Grading in progress..."
+    try:
+        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")
+
+        model = genai.GenerativeModel("gemini-2.5-pro", generation_config={"temperature": 0})
+        resp = model.generate_content([GRADING_PROMPT, qp_uploaded, ms_uploaded, transcription])
+
+        grading = getattr(resp, "text", None) or resp.candidates[0].content.parts[0].text
+
+        yield grading
+    except Exception as e:
+        yield f"❌ Error during grading: {e}"
+
+# ---------- GRADIO APP ----------
+with gr.Blocks(title="📘 AI Teacher Assistant") as demo:
+    gr.Markdown("## 📘 AI Teacher Assistant\nUpload exam documents to transcribe and grade student answers step by step.")
+
+    with gr.Row():
+        qp_file = gr.File(label="Upload Question Paper (PDF)", type="filepath")
+        ms_file = gr.File(label="Upload Mark Scheme (PDF)", type="filepath")
+        ans_file = gr.File(label="Upload Student Answer Sheet (PDF)", type="filepath")
+
+    # Step 1: Transcription
+    transcribe_btn = gr.Button("Step 1: Transcribe Answer Sheet")
+    transcription_out = gr.Markdown(label="📄 Student Transcription", elem_id="transcription_box")
+
+    # Step 2: Grading
+    grade_btn = gr.Button("Step 2: Grade the Student")
+    grading_out = gr.Textbox(label="✅ Grading Report (Step-by-Step)", lines=20)
+
+    # Button Logic with streaming updates
+    transcribe_btn.click(fn=transcribe, inputs=[ans_file], outputs=[transcription_out])
+    grade_btn.click(fn=grade, inputs=[qp_file, ms_file, transcription_out], outputs=[grading_out])
+
+if __name__ == "__main__":
+    demo.launch()