Update app.py

app.py

@@ -1,63 +1,66 @@
 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")
-
+# df = pd.read_csv("Diemthi2024_processed.csv")
 
 def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+    sbd, khoi
 ):
-    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 = ""
+    # score = df[df['sbd_str'] == sbd]
+    # count_all = (df[khoi] > score[khoi].iloc[0]).sum()
+    # count_kv = ((df[khoi] > score[khoi].iloc[0]) & (df['kv'] == score['kv'].iloc[0])).sum()
+    # count_tinh = ((df[khoi] > score[khoi].iloc[0]) & (df['tinh'] == score['tinh'].iloc[0])).sum()
 
-    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
+    count_all = 0
+    count_kv = 0
+    count_tinh = 0
 
-        response += token
-        yield response
+    return f"""
+    National Ranking: {count_all}
+    Regional Ranking: {count_kv}
+    Provincal Ranking: {count_tinh}"""
 
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+Chatbot
 """
-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)",
-        ),
-    ],
-)
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        <style>
+        .gr-button-secondary {
+            width: 100px;
+            height: 30px;
+            padding: 5px;
+        }
+        .gr-row {
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+        .gr-block {
+            padding: 20px;
+        }
+        .gr-markdown p {
+            font-size: 16px;
+        }
+        </style>
+        <span style='font-family: Arial, sans-serif; font-size: 20px;'>National Graduation Exam Score Ranking</span>
+        <p style='font-family: Arial, sans-serif;'>Input your ID and your combination:</p>
+        """
+    )
+    with gr.Row():
+        id = gr.Textbox(placeholder="Input your ID", label="", lines=1)
+        comb = gr.Dropdown(
+                ["A00", "A01", "B00", "C00", "C03", "D01", "D07"], label="Combination", info="Will add more combinations later!"
+            )
+    
+    with gr.Row():
+        check_button = gr.Button("Rank now!", variant="primary")
 
+    out = gr.Textbox(label="OUTPUT", placeholder="", lines=2)
+    
+    check_button.click(fn=respond, inputs=[id,comb], outputs=out)
 
 if __name__ == "__main__":
     demo.launch()