Create app.py

app.py

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+import gradio as gr
+from openai import OpenAI
+import os
+
+ACCESS_TOKEN = os.getenv("HF_TOKEN")
+
+client = OpenAI(
+    base_url="https://api-inference.huggingface.co/v1/", 
+    api_key=ACCESS_TOKEN,
+)
+
+*def* *generate_study_material*(
+    topic, 
+    difficulty, 
+    question_type, 
+    focus_areas, 
+    anxiety_level,
+    num_questions
+):
+    # Customize prompt based on anxiety level and learning focus
+    anxiety_prompts = {
+        "High": "Create a gradual, confidence-building set of questions. Start with easier concepts and progressively increase difficulty. Include encouraging notes.",
+        "Medium": "Balance challenge with achievability. Include hints for tougher questions and positive reinforcement.",
+        "Low": "Focus on comprehensive concept testing while maintaining an encouraging tone."
+    }
+    
+    focus_prompt = {
+        "concept_understanding": "Emphasize questions that test deep understanding rather than memorization.",
+        "problem_solving": "Include scenario-based questions that require analytical thinking.",
+        "quick_recall": "Focus on key definitions and fundamental concepts.",
+        "practical_application": "Create questions based on real-world applications."
+    }
+
+    base_prompt = f"""
+    Act as an expert educational psychologist and subject matter expert creating an exam preparation guide.
+    Topic: {topic}
+    Difficulty: {difficulty}
+    Question Type: {question_type}
+    Number of Questions: {num_questions}
+    
+    Special Considerations:
+    - Anxiety Level: {anxiety_level}
+    {anxiety_prompts[anxiety_level]}
+    
+    - Learning Focus: {focus_areas}
+    {focus_prompt[focus_areas]}
+    Generate questions following these guidelines:
+    1. Start with a brief confidence-building message
+    2. Include clear, unambiguous questions
+    3. Provide detailed explanations for each answer
+    4. Add study tips relevant to the topic
+    5. Include a "Remember" section with key points
+    
+    Format:
+    - For Multiple Choice: Include 4 options with explanations for each
+    - For Short Answer: Provide structure hints and model answers
+    - For Descriptive: Break down marking criteria and include outline points
+    
+    Additional Requirements:
+    - Include think-aloud strategies for problem-solving
+    - Add time management suggestions
+    - Highlight common misconceptions to avoid
+    - End with a positive reinforcement message
+    """
+
+    *try*:
+        messages = [
+            {"role": "system", "content": "You are an expert educational content generator."},
+            {"role": "user", "content": base_prompt}
+        ]
+        
+        response = client.chat.completions.create(
+            model="Qwen/QwQ-32B-Preview",
+            messages=messages,
+            max_tokens=2048,
+            temperature=0.7,
+            top_p=0.9
+        )
+        
+        *return* response.choices[0].message.content
+    
+    *except* *Exception* *as* e:
+        *return* f"An error occurred: {*str*(e)}\nPlease try again with different parameters."
+
+*def* *create_interface*():
+    *with* gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) *as* iface:
+        gr.Markdown("""
+        # <div align="center"><strong>📚 Exam Preparation Assistant</strong></div>
+        
+        Welcome to your personalized exam preparation assistant! This tool is designed to help you:
+        - Build confidence through practiced learning
+        - Understand concepts deeply
+        - Reduce exam anxiety through structured practice
+        
+        Remember: Every practice session brings you closer to mastery! 🌟
+        """)
+        
+        *with* gr.Row():
+            *with* gr.Column():
+                topic = gr.Textbox(
+                    label="Topic or Subject",
+                    placeholder="Enter the topic you want to study (e.g., 'Python Lists and Tuples', 'Chemical Bonding')",
+                    lines=2
+                )
+                
+                difficulty = gr.Radio(
+                    choices=["Beginner", "Intermediate", "Advanced"],
+                    label="Difficulty Level",
+                    value="Intermediate",
+                    info="Choose based on your current understanding"
+                )
+                
+                question_type = gr.Radio(
+                    choices=["Multiple Choice", "Short Answer", "Descriptive"],
+                    label="Question Type",
+                    value="Multiple Choice",
+                    info="Select the format that best helps your learning"
+                )
+                
+                focus_areas = gr.Radio(
+                    choices=[
+                        "concept_understanding",
+                        "problem_solving",
+                        "quick_recall",
+                        "practical_application"
+                    ],
+                    label="Learning Focus",
+                    value="concept_understanding",
+                    info="What aspect do you want to improve?"
+                )
+                
+                anxiety_level = gr.Radio(
+                    choices=["High", "Medium", "Low"],
+                    label="Current Anxiety Level",
+                    value="Medium",
+                    info="This helps us adjust the difficulty progression"
+                )
+                
+                num_questions = gr.Slider(
+                    minimum=3,
+                    maximum=10,
+                    value=5,
+                    step=1,
+                    label="Number of Questions"
+                )
+                
+                submit_btn = gr.Button(
+                    "Generate Study Material",
+                    variant="primary"
+                )
+
+            *with* gr.Column():
+                output = gr.Textbox(
+                    label="Your Personalized Study Material",
+                    lines=20,
+                    show_copy_button=True
+                )
+        
+        
+        submit_btn.click(
+            *fn*=generate_study_material,
+            inputs=[
+                topic,
+                difficulty,
+                question_type,
+                focus_areas,
+                anxiety_level,
+                num_questions
+            ],
+            outputs=output
+        )
+    
+    *return* iface
+
+*if* **name** == "__main__":
+    iface = create_interface()
+    iface.launch()