Update app.py

app.py

@@ -1,127 +1,192 @@
-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")
+# 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)
 
-################################################################
+# ################################################################
 
-# Load your CSV file
-df = pd.read_csv("your_file.csv")
+# # Combine both interfaces into a tabbed layout
+# tabbed_interface = gr.TabbedInterface(
+#     [dropdown_interface, demo],
+#     ["Problem Solver", "Chat Interface"]
+# )
 
-# 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")
+# # Launch the app
+# if __name__ == "__main__":
+#     tabbed_interface.launch()
 
-# 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
+import gradio as gr
+from huggingface_hub import InferenceClient
 
-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
+"""
+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")
 
-################################################################
 
 def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+message,
+history: list[tuple[str, str]],
+system_message,
+max_tokens,
+temperature,
+top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
+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]})
+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})
+messages.append({"role": "user", "content": message})
 
-    response = ""
+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
+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
+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"]
+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)",
+),
+],
 )
 
-################################################################
 
-# Launch the app
 if __name__ == "__main__":
-    tabbed_interface.launch()
+demo.launch()