Update app.py

app.py

@@ -1,66 +1,87 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# Initialize Hugging Face Inference Client
+# Initialize the Hugging Face Inference Client
 client = InferenceClient()
 
-# Function to use a model for generating content dynamically
-def generate_content(selected_path):
+# Function to stream content for Math, STEM, and Code Generation
+def generate_stream(selected_topic, input_text):
     """
-    Generates dynamic content based on the selected learning path using an NLP model.
-    """
-    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"]
+    Generates dynamic lessons, solutions, or code snippets based on the selected topic.
 
-# 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.
+    Args:
+        selected_topic (str): The selected subject (e.g., Math, STEM, or Code Generation).
+        input_text (str): Additional input for contextual content generation.
+
+    Yields:
+        str: Incremental output content.
     """
-    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
+    # Create a topic-specific prompt
+    prompt = (
+        f"Generate a {selected_topic.lower()} lesson, problem, or example based on the following input: {input_text}"
+        if input_text.strip() else
+        f"Generate a beginner-level {selected_topic.lower()} lesson with examples."
     )
-    return response["generated_text"]
+    messages = [{"role": "user", "content": prompt}]
 
-# Gradio interface
+    try:
+        # Create a stream for generating content
+        stream = client.chat.completions.create(
+            model="Qwen/Qwen2.5-Coder-32B-Instruct",  # Streaming model
+            messages=messages,
+            temperature=0.5,
+            max_tokens=1024,
+            top_p=0.7,
+            stream=True
+        )
+
+        # Stream the generated content incrementally
+        generated_content = ""
+        for chunk in stream:
+            generated_content += chunk.choices[0].delta.content
+            yield generated_content  # Yield content incrementally
+    except Exception as e:
+        yield f"Error: {e}"  # Display error if any issues occur
+
+# Create the Gradio interface
 with gr.Blocks() as app:
-    gr.Markdown("## AI-Powered Learning Platform")
-    gr.Markdown("Choose a topic to start learning:")
-    
-    # Progress bar at the top (HTML-based)
-    gr.HTML('<div style="width: 100%; background-color: lightgray; height: 5px; border-radius: 5px;">'
-            '<div style="width: 25%; background-color: green; height: 100%;"></div></div>')
-    
-    # Options for learning paths
-    selected_topic = gr.Radio(
-        choices=["Math", "Science & Engineering", "Computer Science & Programming", "Data Science & Data Analysis"],
-        label="Select a Learning Path",
-        value="Math"
+    # App Title and Instructions
+    gr.Markdown("## 🎓 STEM Learning and Code Generator")
+    gr.Markdown(
+        "Get dynamic lessons, problem-solving examples, or code snippets for Math, STEM, "
+        "or Computer Science. Select a topic and get started!"
     )
-    
-    # Buttons for user interaction
+
     with gr.Row():
-        generate_button = gr.Button("Generate Content")
-        submit_answer_button = gr.Button("Submit Answer")
+        # Input Section
+        with gr.Column():
+            selected_topic = gr.Radio(
+                choices=["Math", "STEM", "Computer Science (Code Generation)"],
+                label="Select a Topic",
+                value="Math"  # Default selection
+            )
+            input_text = gr.Textbox(
+                lines=2,
+                label="Optional Input",
+                placeholder="Provide additional context (e.g., 'Explain calculus basics' or 'Generate Python code for sorting')."
+            )
+            generate_button = gr.Button("Generate Content")
 
-    # Outputs
-    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")
+        # Output Section
+        with gr.Column():
+            gr.Markdown("### Generated Content")
+            output_stream = gr.Textbox(
+                lines=15,
+                label="Output",
+                interactive=False
+            )
 
-    # Link interactions to actions
-    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)
+    # Link the generate button to the streaming function
+    generate_button.click(
+        fn=generate_stream,
+        inputs=[selected_topic, input_text],
+        outputs=output_stream
+    )
 
-# Launch the app
+# Launch the Gradio app
 app.launch()