Update app.py
Browse files
app.py
CHANGED
@@ -8,7 +8,7 @@ client = InferenceClient()
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# Function to generate dynamic lessons, examples, or code
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def generate_content(selected_topic, subtopic, complexity, input_text, examples_count, output_type):
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"""
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Generate content dynamically based on user input.
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Args:
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selected_topic (str): Topic selected by the user.
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@@ -16,10 +16,10 @@ def generate_content(selected_topic, subtopic, complexity, input_text, examples_
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complexity (str): User expertise level.
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input_text (str): Additional input context.
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examples_count (int): Number of examples to generate.
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output_type (str): Desired output format.
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Returns:
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"""
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# Build the prompt dynamically
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prompt = (
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@@ -43,7 +43,9 @@ def generate_content(selected_topic, subtopic, complexity, input_text, examples_
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# Adjust the output based on the selected type
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if output_type == "LaTeX":
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elif output_type == "Downloadable":
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt")
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with open(temp_file.name, "w") as file:
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@@ -56,11 +58,11 @@ def generate_content(selected_topic, subtopic, complexity, input_text, examples_
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# Create the Gradio interface
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with gr.Blocks() as app:
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# App Title
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gr.Markdown("## π Advanced STEM and Code Generator with
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with gr.Row():
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# Input
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with gr.Column():
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selected_topic = gr.Radio(
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choices=["Math", "STEM", "Code Generation"],
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@@ -95,24 +97,31 @@ with gr.Blocks() as app:
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)
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generate_button = gr.Button("Generate Content")
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# Output
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with gr.Column():
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gr.Markdown("### π Output")
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label="Generated Output",
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lines=15,
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interactive=False
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)
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download_button = gr.File(label="Download File (if applicable)")
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#
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generate_button.click(
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fn=generate_content,
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inputs=[selected_topic, subtopic, complexity, input_text, examples_count, output_type],
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outputs=[
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)
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# Feedback
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feedback_label = gr.Label(value="Was this content helpful?")
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feedback_rating = gr.Radio(
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choices=["Yes", "No"],
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# Function to generate dynamic lessons, examples, or code
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def generate_content(selected_topic, subtopic, complexity, input_text, examples_count, output_type):
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"""
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Generate content dynamically based on user input, with support for LaTeX rendering and file downloads.
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Args:
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selected_topic (str): Topic selected by the user.
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complexity (str): User expertise level.
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input_text (str): Additional input context.
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examples_count (int): Number of examples to generate.
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output_type (str): Desired output format (Plain Text, LaTeX, or Downloadable).
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Returns:
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dict or str: Generated content in the desired format.
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"""
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# Build the prompt dynamically
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prompt = (
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# Adjust the output based on the selected type
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if output_type == "LaTeX":
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# Wrap content in LaTeX delimiters for rendering
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latex_content = f"$$\n{content}\n$$"
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return {"content": latex_content, "latex": True}
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elif output_type == "Downloadable":
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt")
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with open(temp_file.name, "w") as file:
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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("## π Advanced STEM and Code Generator with LaTeX Rendering and Downloads")
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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", "Code Generation"],
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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("### π Generated Output (Supports LaTeX)")
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output_text = gr.Markdown(label="Generated Content")
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download_button = gr.File(label="Download File (if applicable)")
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# Connect the generate function to the button
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def update_output(result):
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if isinstance(result, dict):
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if "latex" in result:
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return result["content"], None
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elif "file" in result:
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return "File ready for download.", result["file"]
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else:
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return result, None
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generate_button.click(
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fn=generate_content,
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inputs=[selected_topic, subtopic, complexity, input_text, examples_count, output_type],
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outputs=[output_text, download_button],
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preprocess=False,
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postprocess=update_output
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)
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# Feedback Section
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feedback_label = gr.Label(value="Was this content helpful?")
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feedback_rating = gr.Radio(
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choices=["Yes", "No"],
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