Update app.py

app.py

@@ -531,7 +531,7 @@ def merge_pdfs(paths, output_path):
         writer.write(f) 
     return output_path 
 
-def gemini_generate_content(prompt_text, file_upload_obj=None, image_obj=None, model_name="gemini-2.5-pro"): 
+def gemini_generate_content(prompt_text, file_upload_obj=None, image_obj=None, model_name="gemini-2.5-pro", fallback_model="gemini-2.5-flash"): 
     """ 
     Send prompt_text and optionally an uploaded file (or an image object/list) to the model using NEW SDK.
     Returns textual response and prints progress. 
@@ -569,10 +569,10 @@ def gemini_generate_content(prompt_text, file_upload_obj=None, image_obj=None, m
     except Exception as e:
         print(f"❌ Generation failed: {e}")
         # Try fallback model
-        print("⚡ Trying fallback model: gemini-2.5-flash")
+        print(f"⚡ Trying fallback model: {fallback_model}")
         try:
             response = client.models.generate_content(
-                model="gemini-2.5-flash",
+                model=fallback_model,
                 contents=contents
             )
             raw_text = response.text
@@ -893,7 +893,7 @@ Grading JSON:
         print("⚠️ Trying fallback model for mapping...")
         contents = [prompt] + images
         response = client.models.generate_content(
-            model="gemini-1.5-flash",
+            model="gemini-2.5-flash-preview-09-2025",
             contents=contents
         )
         raw_text = response.text
@@ -1092,7 +1092,7 @@ def align_and_grade_pipeline(qp_path, ms_path, ans_path, subject="Maths", imprin
 
         print("1.i) Transcribing QP+MS (questions first, then full markscheme, with graph detection)...")
         qpms_prompt = QP_MS_TRANSCRIPTION_PROMPT["content"] + "\nAt the end, also list all questions in the markscheme where a graph is expected, in the format:\nGraph expected in:\n- Question <number> → Page <number>\n(One per line, after ==== MARKSCHEME END ====)"
-        qpms_text = gemini_generate_content(qpms_prompt, file_upload_obj=merged_uploaded, model_name="gemini-2.5-flash")
+        qpms_text = gemini_generate_content(qpms_prompt, file_upload_obj=merged_uploaded, model_name="gemini-2.5-flash", fallback_model="gemini-2.5-flash-preview-09-2025")
         print("📄 QP+MS transcription received. Saving debug file: debug_qpms_transcript.txt")
         with open("debug_qpms_transcript.txt", "w", encoding="utf-8") as f:
             f.write(qpms_text)
@@ -1110,7 +1110,7 @@ def align_and_grade_pipeline(qp_path, ms_path, ans_path, subject="Maths", imprin
 
         print("1.ii) Building AS transcription prompt with expected question IDs and graph detection, sending to Gemini...")
         as_prompt = build_as_cot_prompt_with_expected_ids(extracted_ids, qpms_text) + "\nAt the end, also list all answers where a graph is found, in the format:\nGraph found in:\n- Answer <number> → Page <number>\n(One per line, after all answers)"
-        as_text = gemini_generate_content(as_prompt, file_upload_obj=ans_uploaded, model_name="gemini-2.5-flash")
+        as_text = gemini_generate_content(as_prompt, file_upload_obj=ans_uploaded, model_name="gemini-2.5-flash", fallback_model="gemini-2.5-flash-preview-09-2025")
         print("📝 AS transcription received. Saving debug file: debug_as_transcript.txt")
         with open("debug_as_transcript.txt", "w", encoding="utf-8") as f:
             f.write(as_text)
@@ -1137,7 +1137,7 @@ def align_and_grade_pipeline(qp_path, ms_path, ans_path, subject="Maths", imprin
         grading_prompt_obj = get_grading_prompt(subject.lower())
         grading_prompt_system = grading_prompt_obj["content"]
         grading_images = ms_graph_images + as_graph_images
-        grading_text = gemini_generate_content(grading_prompt_system + "\n\nPlease grade the following transcripts:\n" + grading_input, image_obj=grading_images if grading_images else None, model_name="gemini-2.5-pro")
+        grading_text = gemini_generate_content(grading_prompt_system + "\n\nPlease grade the following transcripts:\n" + grading_input, image_obj=grading_images if grading_images else None, model_name="gemini-2.5-pro", fallback_model="gemini-2.5-flash")
         print("🧾 Grading output received. Saving debug file: debug_grading.md")
         with open("debug_grading.md", "w", encoding="utf-8") as f:
             f.write(grading_text)
@@ -1189,9 +1189,9 @@ def align_and_grade_pipeline(qp_path, ms_path, ans_path, subject="Maths", imprin
         return f"❌ Error: {e}", None, None, None, None, {}
 
 # ---------------- GRADIO UI ---------------- 
-with gr.Blocks(title="AI Grading (Pandoc + pdflatex)") as demo: 
-    gr.Markdown("## 📘 AI Grading — Using Pandoc + pdflatex for PDF Generation") 
-    gr.Markdown("**✅ Now using Pandoc with pdflatex for professional-quality PDF outputs!**")
+with gr.Blocks(title="AI Grading") as demo: 
+    gr.Markdown("## 📘 AI Grading ") 
+  
     
     if supabase_client:
         gr.Markdown("**☁️ Supabase Storage: Enabled** - All files will be uploaded to cloud storage")