Update app.py

app.py

@@ -1,97 +1,40 @@
+!pip install transformers accelerate gradio
+from transformers import pipeline
 import gradio as gr
-import pandas as pd
-import os
-import google.generativeai as genai
-import fitz  # PyMuPDF to read PDF
 
-# Load Gemini API key from Hugging Face secret
-genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
-model = genai.GenerativeModel("gemini-pro")
+generator = pipeline("text-generation", model="tiiuae/falcon-rw-1b")  # Or try flan-t5-base
 
-# PDF reading utility
-def extract_text_from_pdf(pdf_file):
-    text = ""
-    doc = fitz.open(pdf_file.name)
-    for page in doc:
-        text += page.get_text()
-    return text.strip()
+prompt = """
+You are a professor of Discrete Mathematics. Generate an original and creative discrete math question (not copied or templated), suitable for university-level students. 
+Include only the question. Avoid multiple-choice format.
 
-# Gemini prompting logic
-def evaluate_prompt(question, solution, instructions):
-    prompt = f"""
-You are an expert math evaluator. Follow these instructions carefully:
-
-{instructions}
-
----
-
-**Question:**
-{question}
-
-**Student's Solution:**
-{solution}
-
-Evaluate the solution. Say whether it's correct or not. If incorrect, give a clear and detailed explanation of why it's wrong and how to fix it.
+Topic: Combinatorics
+Difficulty: Medium
 
-Respond in this format:
-Correct?: [Yes/No]
-Reasoning: <explanation>
+Question:
 """
-    response = model.generate_content(prompt)
-    return response.text.strip()
+response = generator(prompt, max_new_tokens=150)[0]['generated_text']
+print(response)
 
-# Handler for single text input
-def evaluate_text_inputs(question, solution, instructions_file, instructions_text):
-    instructions = instructions_text or extract_text_from_pdf(instructions_file) if instructions_file else ""
-    result = evaluate_prompt(question, solution, instructions)
-    return result
 
-# Handler for CSV batch input
-def evaluate_csv(csv_file, instructions_file, instructions_text):
-    instructions = instructions_text or extract_text_from_pdf(instructions_file) if instructions_file else ""
-    df = pd.read_csv(csv_file.name)
-    
-    evaluations = []
-    for _, row in df.iterrows():
-        q = row.get('question', '')
-        s = row.get('solution', '')
-        try:
-            eval_result = evaluate_prompt(q, s, instructions)
-            evaluations.append(eval_result)
-        except Exception as e:
-            evaluations.append(f"Error: {e}")
-    
-    df['evaluation'] = evaluations
-    output_path = "evaluated_results.csv"
-    df.to_csv(output_path, index=False)
-    return output_path
 
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 Solution Evaluator (Gemini API)")
-    gr.Markdown("Evaluate student solutions using Google's Gemini API.")
-
-    with gr.Tab("📝 Single Evaluation"):
-        with gr.Row():
-            question_input = gr.Textbox(label="Question", lines=3)
-            solution_input = gr.Textbox(label="Solution", lines=6)
-        with gr.Row():
-            instructions_text = gr.Textbox(label="Instructions (text)", lines=6)
-            instructions_file = gr.File(label="Upload Instructions PDF", file_types=[".pdf"])
-        output_single = gr.Textbox(label="Evaluation Result", lines=8)
-        btn_single = gr.Button("Evaluate Solution")
-        btn_single.click(fn=evaluate_text_inputs,
-                         inputs=[question_input, solution_input, instructions_file, instructions_text],
-                         outputs=output_single)
-
-    with gr.Tab("📄 Batch CSV Evaluation"):
-        csv_file = gr.File(label="Upload CSV (columns: question, solution)", file_types=[".csv"])
-        inst_text = gr.Textbox(label="Instructions (text)", lines=6)
-        inst_pdf = gr.File(label="Upload Instructions PDF", file_types=[".pdf"])
-        output_csv = gr.File(label="Download Evaluated CSV")
-        btn_batch = gr.Button("Evaluate All")
-        btn_batch.click(fn=evaluate_csv,
-                        inputs=[csv_file, inst_pdf, inst_text],
-                        outputs=output_csv)
-
-demo.launch()
+def generate_question(topic, difficulty):
+    prompt = f"""
+You are a professor of Discrete Mathematics. Generate an original and creative discrete math question.
+Topic: {topic}
+Difficulty: {difficulty}
+Question:
+"""
+    result = generator(prompt, max_new_tokens=150)[0]['generated_text']
+    return result.split("Question:")[-1].strip()
+
+topics = ["Combinatorics", "Logic", "Set Theory", "Functions", "Relations", "Graph Theory"]
+difficulties = ["Easy", "Medium", "Hard"]
+
+gr.Interface(
+    fn=generate_question,
+    inputs=[gr.Dropdown(choices=topics, label="Select Topic"),
+            gr.Dropdown(choices=difficulties, label="Select Difficulty")],
+    outputs="text",
+    title="Discrete Math Question Generator"
+).launch()