Create app.py

app.py

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+# import gradio as gr
+# from huggingface_hub import InferenceClient
+# import pandas as pd
+
+# """
+# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+# """
+# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# ################################################################
+
+# # Load your CSV file
+# df = pd.read_csv("your_file.csv")
+
+# # Create dropdowns for exam name, year, and problem number
+# exam_names = df["exam name"].unique()
+# year_options = df["year"].unique()
+# problem_numbers = df["problem number"].unique()
+
+# exam_dropdown = gr.Dropdown(exam_names, label="Exam Name")
+# year_dropdown = gr.Dropdown(year_options, label="Year")
+# problem_dropdown = gr.Dropdown(problem_numbers, label="Problem Number")
+
+# # Define the functions for the three buttons
+# def solve_problem(exam, year, problem):
+#     problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0]
+#     prompt = f"Solve the following problem: {problem_statement}"
+#     response = client.chat_completion(prompt, max_tokens=512, temperature=0.7, top_p=0.95)
+#     return response.choices[0].text
+
+# def give_hints(exam, year, problem):
+#     problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0]
+#     prompt = f"Give hints for the following problem: {problem_statement}"
+#     response = client.chat_completion(prompt, max_tokens=512, temperature=0.7, top_p=0.95)
+#     return response.choices[0].text
+
+# def create_similar_problem(exam, year, problem):
+#     problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0]
+#     prompt = f"Create a similar problem to the following one: {problem_statement}"
+#     response = client.chat_completion(prompt, max_tokens=512, temperature=0.7, top_p=0.95)
+#     return response.choices[0].text
+
+# ################################################################
+
+# def respond(
+#     message,
+#     history: list[tuple[str, str]],
+#     system_message,
+#     max_tokens,
+#     temperature,
+#     top_p,
+# ):
+#     messages = [{"role": "system", "content": system_message}]
+
+#     for val in history:
+#         if val[0]:
+#             messages.append({"role": "user", "content": val[0]})
+#         if val[1]:
+#             messages.append({"role": "assistant", "content": val[1]})
+
+#     messages.append({"role": "user", "content": message})
+
+#     response = ""
+
+#     for message in client.chat_completion(
+#         messages,
+#         max_tokens=max_tokens,
+#         stream=True,
+#         temperature=temperature,
+#         top_p=top_p,
+#     ):
+#         token = message.choices[0].delta.content
+
+#         response += token
+#         yield response
+
+# """
+# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+# """
+# demo = gr.ChatInterface(
+#     respond,
+#     additional_inputs=[
+#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#         gr.Slider(
+#             minimum=0.1,
+#             maximum=1.0,
+#             value=0.95,
+#             step=0.05,
+#             label="Top-p (nucleus sampling)",
+#         ),
+#     ],
+# )
+
+# ################################################################
+
+# # Create Gradio interface with Blocks context
+# with gr.Blocks() as dropdown_interface:
+#     with gr.Column():
+#         exam_dropdown.render()
+#         year_dropdown.render()
+#         problem_dropdown.render()
+        
+#         solve_button = gr.Button("Solve Problem")
+#         hints_button = gr.Button("Give Hints")
+#         similar_problem_button = gr.Button("Create Similar Problem")
+        
+#         output_text = gr.Textbox(label="Output")
+    
+#         solve_button.click(solve_problem, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text)
+#         hints_button.click(give_hints, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text)
+#         similar_problem_button.click(create_similar_problem, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text)
+
+# ################################################################
+
+# # Combine both interfaces into a tabbed layout
+# tabbed_interface = gr.TabbedInterface(
+#     [dropdown_interface, demo],
+#     ["Problem Solver", "Chat Interface"]
+# )
+
+# ################################################################
+
+# # Launch the app
+# if __name__ == "__main__":
+#     tabbed_interface.launch()
+
+
+import pandas as pd
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Initialize the InferenceClient
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# Load your CSV file
+df = pd.read_csv("your_file.csv")
+
+# Create dropdowns for exam name, year, and problem number
+exam_names = df["exam name"].unique()
+year_options = df["year"].unique()
+problem_numbers = df["problem number"].unique()
+
+exam_dropdown = gr.Dropdown(exam_names, label="Exam Name")
+year_dropdown = gr.Dropdown(year_options, label="Year")
+problem_dropdown = gr.Dropdown(problem_numbers, label="Problem Number")
+
+# Define the functions for the three buttons
+def solve_problem(exam, year, problem):
+    problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0]
+    prompt = f"Solve the following problem: {problem_statement}"
+    response = client.text_generation(prompt, max_new_tokens=512, temperature=0.7, top_p=0.95)
+    return response[0]['generated_text']
+
+def give_hints(exam, year, problem):
+    problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0]
+    prompt = f"Give hints for the following problem: {problem_statement}"
+    response = client.text_generation(prompt, max_new_tokens=512, temperature=0.7, top_p=0.95)
+    return response[0]['generated_text']
+
+def create_similar_problem(exam, year, problem):
+    problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0]
+    prompt = f"Create a similar problem to the following one: {problem_statement}"
+    response = client.text_generation(prompt, max_new_tokens=512, temperature=0.7, top_p=0.95)
+    return response[0]['generated_text']
+
+# Define the chat response function
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
+
+# Create Gradio interface with Blocks context
+with gr.Blocks() as dropdown_interface:
+    with gr.Column():
+        exam_dropdown.render()
+        year_dropdown.render()
+        problem_dropdown.render()
+        
+        solve_button = gr.Button("Solve Problem")
+        hints_button = gr.Button("Give Hints")
+        similar_problem_button = gr.Button("Create Similar Problem")
+        
+        output_text = gr.Textbox(label="Output")
+    
+        solve_button.click(solve_problem, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text)
+        hints_button.click(give_hints, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text)
+        similar_problem_button.click(create_similar_problem, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text)
+
+chat_interface = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
+
+# Combine both interfaces into a tabbed layout
+tabbed_interface = gr.TabbedInterface(
+    [dropdown_interface, chat_interface],
+    ["Problem Solver", "Chat Interface"]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    tabbed_interface.launch()