Update app.py

app.py

@@ -2,7 +2,7 @@ import os
 import gradio as gr
 import google.generativeai as genai
 
-# ✅ Configure Gemini with Hugging Face Secret
+# Configure Gemini API (key must be set in Hugging Face Space secrets)
 genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
 
 # ---------- PROMPTS ----------
@@ -34,44 +34,54 @@ Rules:
 6. Do not give marks outside the markscheme.
 Output must be a structured grading report with reasoning.
 """
-def grade_student(qp_file, ms_file, ans_file):
-    try:
-        # Get file paths
-        qp_path = qp_file.name
-        ms_path = ms_file.name
-        ans_path = ans_file.name
 
-        # Upload to Gemini
-        qp_uploaded = genai.upload_file(path=qp_path, display_name="Question Paper")
-        ms_uploaded = genai.upload_file(path=ms_path, display_name="Marking Scheme")
-        ans_uploaded = genai.upload_file(path=ans_path, display_name="Answer Sheet")
+# ---------- STEP 1: TRANSCRIPTION ----------
+def transcribe(ans_file):
+    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
+        return transcription
+    except Exception as e:
+        return f"❌ Error during transcription: {e}"
 
-        transcription_resp = model.generate_content([TRANSCRIPTION_PROMPT, ans_uploaded])
-        transcription = getattr(transcription_resp, "text", None) or transcription_resp.candidates[0].content.parts[0].text
+# ---------- STEP 2: GRADING ----------
+def grade(qp_file, ms_file, transcription):
+    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")
 
-        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
+        model = genai.GenerativeModel("gemini-2.5-pro", generation_config={"temperature": 0})
+        resp = model.generate_content([GRADING_PROMPT, qp_uploaded, ms_uploaded, transcription])
 
-        return transcription, grading
+        grading = getattr(resp, "text", None) or resp.candidates[0].content.parts[0].text
+        return grading
     except Exception as e:
-        return f"❌ Error during processing: {e}", ""
-
-demo = gr.Interface(
-    fn=grade_student,
-    inputs=[
-        gr.File(label="Upload Question Paper (PDF)", type="filepath"),
-        gr.File(label="Upload Mark Scheme (PDF)", type="filepath"),
-        gr.File(label="Upload Student Answer Sheet (PDF)", type="filepath"),
-    ],
-    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 and grades answers."
-)
+        return 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")
+
+    # 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
+    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()
+    demo.launch()