Update app.py
Browse files
app.py
CHANGED
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import gradio as gr
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from huggingface_hub import InferenceClient
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# Initialize Hugging Face Inference Client
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client = InferenceClient()
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# Function to
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def
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"""
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Generates dynamic
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"""
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prompt = f"Create a short lesson or problem for the topic: {selected_path}."
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response = client.text_generation(
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model="gpt2", # Replace with a more suitable Hugging Face model for educational tasks
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inputs=prompt,
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max_new_tokens=150,
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temperature=0.7
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)
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return response["generated_text"]
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"""
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temperature=0.5
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)
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with gr.Blocks() as app:
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gr.Markdown("
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'<div style="width: 25%; background-color: green; height: 100%;"></div></div>')
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# Options for learning paths
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selected_topic = gr.Radio(
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choices=["Math", "Science & Engineering", "Computer Science & Programming", "Data Science & Data Analysis"],
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label="Select a Learning Path",
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value="Math"
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)
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# Buttons for user interaction
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with gr.Row():
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# Link
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generate_button.click(
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# Launch the app
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app.launch()
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import gradio as gr
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from huggingface_hub import InferenceClient
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# Initialize the Hugging Face Inference Client
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client = InferenceClient()
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# Function to stream content for Math, STEM, and Code Generation
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def generate_stream(selected_topic, input_text):
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"""
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Generates dynamic lessons, solutions, or code snippets based on the selected topic.
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Args:
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selected_topic (str): The selected subject (e.g., Math, STEM, or Code Generation).
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input_text (str): Additional input for contextual content generation.
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Yields:
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str: Incremental output content.
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"""
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# Create a topic-specific prompt
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prompt = (
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f"Generate a {selected_topic.lower()} lesson, problem, or example based on the following input: {input_text}"
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if input_text.strip() else
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f"Generate a beginner-level {selected_topic.lower()} lesson with examples."
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)
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messages = [{"role": "user", "content": prompt}]
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try:
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# Create a stream for generating content
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stream = client.chat.completions.create(
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model="Qwen/Qwen2.5-Coder-32B-Instruct", # Streaming model
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messages=messages,
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temperature=0.5,
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max_tokens=1024,
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top_p=0.7,
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stream=True
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)
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# Stream the generated content incrementally
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generated_content = ""
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for chunk in stream:
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generated_content += chunk.choices[0].delta.content
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yield generated_content # Yield content incrementally
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except Exception as e:
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yield f"Error: {e}" # Display error if any issues occur
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# Create the Gradio interface
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with gr.Blocks() as app:
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# App Title and Instructions
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gr.Markdown("## 🎓 STEM Learning and Code Generator")
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gr.Markdown(
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"Get dynamic lessons, problem-solving examples, or code snippets for Math, STEM, "
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"or Computer Science. Select a topic and get started!"
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)
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with gr.Row():
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# Input Section
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with gr.Column():
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selected_topic = gr.Radio(
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choices=["Math", "STEM", "Computer Science (Code Generation)"],
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label="Select a Topic",
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value="Math" # Default selection
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)
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input_text = gr.Textbox(
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lines=2,
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label="Optional Input",
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placeholder="Provide additional context (e.g., 'Explain calculus basics' or 'Generate Python code for sorting')."
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)
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generate_button = gr.Button("Generate Content")
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# Output Section
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with gr.Column():
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gr.Markdown("### Generated Content")
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output_stream = gr.Textbox(
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lines=15,
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label="Output",
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interactive=False
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)
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# Link the generate button to the streaming function
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generate_button.click(
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fn=generate_stream,
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inputs=[selected_topic, input_text],
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outputs=output_stream
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)
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# Launch the Gradio app
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app.launch()
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