switch to streamlit

README.md

@@ -3,7 +3,7 @@ title: Code Clippy Problem Solver
 emoji: 💻
 colorFrom: blue
 colorTo: green
-sdk: gradio
+sdk: streamlit
 app_file: app.py
 pinned: false
 ---

app.py

@@ -1,41 +1,33 @@
-import gradio as gr
-
-from rich.console import Console
-from rich.syntax import Syntax
+import streamlit as st
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 # model_name = "flax-community/gpt-code-clippy-1.3B-apps-alldata"
 model_name = "flax-community/gpt-code-clippy-125M-apps-alldata"
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-tokenizer.pad_token = tokenizer.eos_token
 
-console = Console(record=True)
+@st.cache(allow_output_mutation=True)
+def get_model():
+    return AutoModelForCausalLM.from_pretrained(model_name)
 
+@st.cache
+def get_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    tokenizer.pad_token = tokenizer.eos_token
+    return tokenizer
 
 def format_input(question, starter_code=""):
-    answer_type = (
-        "\nUse Call-Based format\n" if starter_code else "\nUse Standard Input format\n"
-    )
+    answer_type = "\nUse Call-Based format\n" if starter_code else \
+                  "\nUse Standard Input format\n"
     return f"\nQUESTION:\n{question}\n{starter_code}\n{answer_type}\nANSWER:\n"
 
 
-def format_outputs(text):
-    formatted_text = Syntax(
-        text, "python", line_numbers=True, indent_guides=True, word_wrap=True
-    )
-    console.print(formatted_text)
-
-    return console.export_html(inline_styles=True)
-
-
-def generate_solution(question, starter_code="", temperature=1.0, num_beams=1):
+def generate_solution(model, tokenizer, question, starter_code="", temperature=1.0, num_beams=1):
     prompt = format_input(question, starter_code)
     input_ids = tokenizer(prompt, return_tensors="pt").input_ids
     start = len(input_ids[0])
+    
     output = model.generate(
         input_ids,
-        max_length=start + 200,
+        max_length=start + 150,
         do_sample=True,
         top_p=0.95,
         pad_token_id=tokenizer.pad_token_id,
@@ -47,9 +39,7 @@ def generate_solution(question, starter_code="", temperature=1.0, num_beams=1):
         num_return_sequences=None,
     )
 
-    return format_outputs(
-        tokenizer.decode(output[0][start:], skip_special_tokens=True).strip()
-    )
+    return tokenizer.decode(output[0][start:], skip_special_tokens=True).strip()
 
 
 _EXAMPLES = [
@@ -87,21 +77,55 @@ def greet(name, owner):
         0.8,
     ],
 ]
+def run():
+    st.set_page_config(
+        page_title="Code Clippy Problem Solver"
+    )
+    # sidebar
+    st.sidebar.title("Code Clippy")
+    st.sidebar.image(
+        "https://raw.githubusercontent.com/ncoop57/gpt-code-clippy/camera-ready/code_clippy_logo.jpg",
+        caption="(c) awesome Aimee Trevett",
+    )
+    st.sidebar.markdown("[Github](https://github.com/ncoop57/gpt-code-clippy)")
+    
+    st.sidebar.markdown("### Controls:")
+    
+    temperature = st.sidebar.slider(
+        "Temperature",
+        min_value=0.5,
+        max_value=1.5,
+        value=0.8,
+        step=0.1,
+    )
+    num_beams = st.sidebar.slider(
+        "Num beams",
+        min_value=1,
+        max_value=4,
+        step=1,
+    )
 
+    # main body
+    model = get_model()
+    tokenizer = get_tokenizer()
 
-inputs = [
-    gr.inputs.Textbox(placeholder="Define a problem here...", lines=7),
-    gr.inputs.Textbox(placeholder="Provide optional starter code...", lines=3),
-    gr.inputs.Slider(0.5, 1.5, 0.1, default=0.8, label="Temperature"),
-    gr.inputs.Slider(1, 4, 1, default=1, label="Beam size"),
-]
+    question = st.text_input(
+        "Problem: ",
+        value="A function that can greet user by name. Given a name it should say hello to user.",
+        help="Text description of the coding problem to be solved",
+    )
+    starter_code = st.text_input(
+        "Started code: ",
+        value="def greet(name):",
+        help="Optional starter code"
+    )
+    submit_button = st.button("Solve")
 
-outputs = [gr.outputs.HTML(label="Solution")]
+    if submit_button:
+        
+        generate_solution(model, tokenizer, question, starter_code, temperature, num_beams)
+        st.code(tmp, language="python")
+    
 
-gr.Interface(
-    generate_solution,
-    inputs=inputs,
-    outputs=outputs,
-    title="Code Clippy: Problem Solver",
-    examples=_EXAMPLES,
-).launch(share=False)
+if __name__=="__main__":
+    run()

gradio_app.py

@@ -0,0 +1,107 @@
+import gradio as gr
+
+from rich.console import Console
+from rich.syntax import Syntax
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# model_name = "flax-community/gpt-code-clippy-1.3B-apps-alldata"
+model_name = "flax-community/gpt-code-clippy-125M-apps-alldata"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+
+console = Console(record=True)
+
+
+def format_input(question, starter_code=""):
+    answer_type = (
+        "\nUse Call-Based format\n" if starter_code else "\nUse Standard Input format\n"
+    )
+    return f"\nQUESTION:\n{question}\n{starter_code}\n{answer_type}\nANSWER:\n"
+
+
+def format_outputs(text):
+    formatted_text = Syntax(
+        text, "python", line_numbers=True, indent_guides=True, word_wrap=True
+    )
+    console.print(formatted_text)
+
+    return console.export_html(inline_styles=True)
+
+
+def generate_solution(question, starter_code="", temperature=1.0, num_beams=1):
+    prompt = format_input(question, starter_code)
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+    start = len(input_ids[0])
+    output = model.generate(
+        input_ids,
+        max_length=start + 200,
+        do_sample=True,
+        top_p=0.95,
+        pad_token_id=tokenizer.pad_token_id,
+        early_stopping=True,
+        temperature=temperature,
+        num_beams=int(num_beams),
+        no_repeat_ngram_size=None,
+        repetition_penalty=None,
+        num_return_sequences=None,
+    )
+
+    return format_outputs(
+        tokenizer.decode(output[0][start:], skip_special_tokens=True).strip()
+    )
+
+
+_EXAMPLES = [
+    [
+        """
+Given a 2D list of size `m * n`. Your task is to find the sum of minimum value in each row.
+For Example:
+```python
+[
+  [1, 2, 3, 4, 5],       # minimum value of row is 1
+  [5, 6, 7, 8, 9],       # minimum value of row is 5
+  [20, 21, 34, 56, 100]  # minimum value of row is 20
+]
+```
+So, the function should return `26` because sum of minimums is as `1 + 5 + 20 = 26`
+        """,
+        "",
+        0.8,
+    ],
+    [
+        """
+# Personalized greeting
+
+Create a function that gives a personalized greeting. This function takes two parameters: `name` and `owner`.
+        """,
+        """
+Use conditionals to return the proper message:
+
+case| return
+--- | ---
+name equals owner | 'Hello boss'
+otherwise         | 'Hello guest'
+def greet(name, owner):
+        """,
+        0.8,
+    ],
+]
+
+
+inputs = [
+    gr.inputs.Textbox(placeholder="Define a problem here...", lines=7),
+    gr.inputs.Textbox(placeholder="Provide optional starter code...", lines=3),
+    gr.inputs.Slider(0.5, 1.5, 0.1, default=0.8, label="Temperature"),
+    gr.inputs.Slider(1, 4, 1, default=1, label="Beam size"),
+]
+
+outputs = [gr.outputs.HTML(label="Solution")]
+
+gr.Interface(
+    generate_solution,
+    inputs=inputs,
+    outputs=outputs,
+    title="Code Clippy: Problem Solver",
+    examples=_EXAMPLES,
+).launch(share=False)

requirements.txt

@@ -1,3 +1,2 @@
 torch
-transformers
-rich
+transformers