Update app.py

app.py

@@ -1,75 +1,53 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-# Initialize the Hugging Face Inference Client
-client = InferenceClient()
-
-# Function to optimize code or simplify mathematical logic
-def optimize_or_simplify(input_text, task_type):
-    """
-    Optimizes a given code snippet or simplifies a mathematical expression based on the selected task.
-
-    Args:
-        input_text (str): The user-provided code or math expression.
-        task_type (str): The type of task ('Code Optimization' or 'Math Simplification').
-
-    Returns:
-        str: The optimized code or simplified mathematical result.
-    """
-    if task_type == "Code Optimization":
-        prompt = (
-            f"Optimize the following code for performance and readability. "
-            f"Provide detailed suggestions and refactor the code:\n\n{input_text}"
-        )
-    else:  # Math Simplification
-        prompt = (
-            f"Simplify the following mathematical expression or algorithm, "
-            f"ensuring accuracy and efficiency:\n\n{input_text}"
-        )
-    
-    # Call the Hugging Face model
-    response = client.chat.completions.create(
-        model="Qwen/Qwen2.5-Coder-32B-Instruct",
-        messages=[{"role": "user", "content": prompt}],
-        temperature=0.5,
-        max_tokens=512
-    )
-    return response["choices"][0]["message"]["content"]
-
-# Create Gradio interface
-with gr.Blocks() as app:
-    gr.Markdown("## Code Optimization and Math Simplification Assistant")
-    gr.Markdown(
-        "Refine your code for better performance or simplify complex mathematical expressions effortlessly. "
-        "Choose a task and input your text to get started!"
-    )
-
-    with gr.Row():
-        # Input section
-        with gr.Column():
-            task_type = gr.Radio(
-                choices=["Code Optimization", "Math Simplification"],
-                label="Select Task",
-                value="Code Optimization"
-            )
-            input_text = gr.Textbox(
-                lines=10,
-                label="Input Text",
-                placeholder="Enter your code or mathematical expression here"
-            )
-            process_button = gr.Button("Process")
-
-        # Output section
-        with gr.Column():
-            gr.Markdown("### Output")
-            output_result = gr.Textbox(lines=15, interactive=False)
-
-    # Link button to function
-    process_button.click(
-        fn=optimize_or_simplify,
-        inputs=[input_text, task_type],
-        outputs=output_result
-    )
-
-# Launch the app
-app.launch()
+import gradio as gr
+
+# Define learning paths and corresponding content
+learning_paths = {
+    "Math": "Welcome to the Math path! Solve problems and explore concepts like algebra, calculus, and geometry.",
+    "Science & Engineering": "Dive into physics, chemistry, and engineering concepts with interactive examples.",
+    "Computer Science & Programming": "Learn programming, algorithms, and computer science fundamentals.",
+    "Data Science & Data Analysis": "Master data manipulation, visualization, and analysis techniques."
+}
+
+# Function to display the selected learning path's content
+def show_learning_content(selected_path):
+    """
+    Displays content for the selected learning path.
+    """
+    return f"You selected: {selected_path}\n\n{learning_paths[selected_path]}"
+
+# Function to simulate feedback or progression
+def provide_feedback(selected_path):
+    return f"Great choice! Let's dive into {selected_path}."
+
+# Gradio interface
+with gr.Blocks() as app:
+    gr.Markdown("## Interactive Learning Platform")
+    gr.Markdown("Choose a topic to start learning:")
+    
+    # Progress bar at the top
+    gr.Markdown('<div style="width: 100%; background-color: lightgray; height: 5px; border-radius: 5px;">'
+                '<div style="width: 25%; background-color: green; height: 100%;"></div></div>', 
+                unsafe_allow_html=True)
+    
+    # Options for learning paths
+    selected_topic = gr.Radio(
+        choices=list(learning_paths.keys()),
+        label="Select a Learning Path",
+        value="Math"
+    )
+    
+    # Buttons for user interaction
+    with gr.Row():
+        continue_button = gr.Button("Continue")
+        feedback_button = gr.Button("Get Feedback")
+
+    # Outputs
+    learning_output = gr.Textbox(lines=8, interactive=False, label="Learning Content")
+    feedback_output = gr.Textbox(interactive=False, label="Feedback")
+
+    # Link interactions to actions
+    continue_button.click(fn=show_learning_content, inputs=selected_topic, outputs=learning_output)
+    feedback_button.click(fn=provide_feedback, inputs=selected_topic, outputs=feedback_output)
+
+# Launch the app
+app.launch()