Update app.py

app.py

@@ -1,27 +1,40 @@
 import gradio as gr
+from huggingface_hub import InferenceClient
 
-# 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."
-}
+# Initialize Hugging Face Inference Client
+client = InferenceClient()
 
-# Function to display the selected learning path's content
-def show_learning_content(selected_path):
+# Function to use a model for generating content dynamically
+def generate_content(selected_path):
     """
-    Displays content for the selected learning path.
+    Generates dynamic content based on the selected learning path using an NLP model.
     """
-    return f"You selected: {selected_path}\n\n{learning_paths[selected_path]}"
+    prompt = f"Create a short lesson or problem for the topic: {selected_path}."
+    response = client.text_generation(
+        model="gpt2",  # Replace with a more suitable Hugging Face model for educational tasks
+        inputs=prompt,
+        max_new_tokens=150,
+        temperature=0.7
+    )
+    return response["generated_text"]
 
-# Function to simulate feedback or progression
-def provide_feedback(selected_path):
-    return f"Great choice! Let's dive into {selected_path}."
+# Function to evaluate user input (for example, math problem answers)
+def evaluate_answer(user_input, selected_path):
+    """
+    Evaluates user input using the model and provides feedback.
+    """
+    prompt = f"Evaluate the following response for {selected_path}:\n\nUser Answer: {user_input}\nProvide feedback."
+    response = client.text_generation(
+        model="gpt2",  # Replace with a more appropriate evaluation model
+        inputs=prompt,
+        max_new_tokens=100,
+        temperature=0.5
+    )
+    return response["generated_text"]
 
 # Gradio interface
 with gr.Blocks() as app:
-    gr.Markdown("## Interactive Learning Platform")
+    gr.Markdown("## AI-Powered Learning Platform")
     gr.Markdown("Choose a topic to start learning:")
     
     # Progress bar at the top (HTML-based)
@@ -30,23 +43,24 @@ with gr.Blocks() as app:
     
     # Options for learning paths
     selected_topic = gr.Radio(
-        choices=list(learning_paths.keys()),
+        choices=["Math", "Science & Engineering", "Computer Science & Programming", "Data Science & Data Analysis"],
         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")
+        generate_button = gr.Button("Generate Content")
+        submit_answer_button = gr.Button("Submit Answer")
 
     # Outputs
-    learning_output = gr.Textbox(lines=8, interactive=False, label="Learning Content")
+    content_output = gr.Textbox(lines=8, interactive=False, label="Generated Content")
+    answer_input = gr.Textbox(lines=2, label="Your Answer")
     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)
+    generate_button.click(fn=generate_content, inputs=selected_topic, outputs=content_output)
+    submit_answer_button.click(fn=evaluate_answer, inputs=[answer_input, selected_topic], outputs=feedback_output)
 
 # Launch the app
 app.launch()