Create app.py

app.py

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+import gradio as gr
+import google.generativeai as genai
+
+# 🔑 Hugging Face Spaces Secret will inject API key
+import os
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+genai.configure(api_key=GEMINI_API_KEY)
+
+model = genai.GenerativeModel("gemini-2.5-pro", generation_config={"temperature": 0})
+
+def transcribe_files(qp_file, ms_file, ans_file):
+    uploaded_as = genai.upload_file(path=ans_file.name, display_name="Answer Sheet")
+    transcription_instructions = """
+    Persona: You are an expert transcriptionist...
+    (same rules as before)
+    """
+    response = model.generate_content([transcription_instructions, uploaded_as])
+    transcription = getattr(response, "text", None)
+    if not transcription and response.candidates:
+        transcription = response.candidates[0].content.parts[0].text
+    return transcription or "No transcription generated."
+
+def grade_files(qp_file, ms_file, ans_file, transcription):
+    uploaded_qp = genai.upload_file(path=qp_file.name, display_name="Question Paper")
+    uploaded_ms = genai.upload_file(path=ms_file.name, display_name="Marking Scheme")
+    grading_system = "Instructions to Examiners..."
+    response = model.generate_content([
+        f"You are an examiner. Grade:\n{grading_system}",
+        uploaded_qp,
+        uploaded_ms,
+        transcription
+    ])
+    grading = getattr(response, "text", None)
+    if not grading and response.candidates:
+        grading = response.candidates[0].content.parts[0].text
+    return grading or "No grading generated."
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 📘 Automated Transcription & Grading System")
+    with gr.Row():
+        qp = gr.File(label="Upload Question Paper (PDF)")
+        ms = gr.File(label="Upload Marking Scheme (PDF)")
+        ans = gr.File(label="Upload Answer Sheet (PDF)")
+    transcribe_btn = gr.Button("🔍 Transcribe")
+    transcription_output = gr.Textbox(label="Transcription", lines=20)
+    grade_btn = gr.Button("✅ Grade")
+    grading_output = gr.Textbox(label="Grading Result", lines=20)
+
+    transcribe_btn.click(fn=transcribe_files, inputs=[qp, ms, ans], outputs=transcription_output)
+    grade_btn.click(fn=grade_files, inputs=[qp, ms, ans, transcription_output], outputs=grading_output)
+
+demo.launch()