Update main.py

main.py

@@ -1,146 +1,110 @@
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-# import random
-
-# models = [
-#     "google/gemma-7b",
-#     "google/gemma-7b-it",
-#     "google/gemma-2b",
-#     "google/gemma-2b-it"
-# ]
-
-# clients = []
-# for model in models:
-#     clients.append(InferenceClient(model))
-
-
-# def format_prompt(message, history):
-#     prompt = ""
-#     if history:
-#         for user_prompt, bot_response in history:
-#             prompt += f"<start_of_turn>user{user_prompt}<end_of_turn>"
-#             prompt += f"<start_of_turn>model{bot_response}"
-#     prompt += f"<start_of_turn>user{message}<end_of_turn><start_of_turn>model"
-#     return prompt
-
-
-# def chat_inf(system_prompt, prompt, history, client_choice, seed, temp, tokens, top_p, rep_p):
-#     client = clients[int(client_choice) - 1]
-#     if not history:
-#         history = []
-#         hist_len = 0
-#     if history:
-#         hist_len = len(history)
-#         print(hist_len)
-
-#     generate_kwargs = dict(
-#         temperature=temp,
-#         max_new_tokens=tokens,
-#         top_p=top_p,
-#         repetition_penalty=rep_p,
-#         do_sample=True,
-#         seed=seed,
-#     )
-#     formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
-#     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
-#                                     return_full_text=False)
-#     output = ""
-
-#     for response in stream:
-#         output += response.token.text
-#         yield [(prompt, output)]
-#     history.append((prompt, output))
-#     yield history
-
-
-# def clear_fn():
-#     return None
-
-
-# rand_val = random.randint(1, 1111111111111111)
-
-
-# def check_rand(inp, val):
-#     if inp is True:
-#         return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
-#     else:
-#         return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))
-
-
-# with gr.Blocks() as app:
-#     gr.HTML(
-#         """<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1></center>""")
-#     with gr.Group():
-#         with gr.Row():
-#             client_choice = gr.Dropdown(label="Models", type='index', choices=[c for c in models], value=models[0],
-#                                         interactive=True)
-#     chat_b = gr.Chatbot(height=500)
-#     with gr.Group():
-#         with gr.Row():
-#             with gr.Column(scale=1):
-#                 with gr.Group():
-#                     rand = gr.Checkbox(label="Random Seed", value=True)
-#                     seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
-#                     tokens = gr.Slider(label="Max new tokens", value=6400, minimum=0, maximum=8000, step=64,
-#                                        interactive=True, visible=True, info="The maximum number of tokens")
-#             with gr.Column(scale=1):
-#                 with gr.Group():
-#                     temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
-#                     top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
-#                     rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)
-
-#     with gr.Group():
-#         with gr.Row():
-#             with gr.Column(scale=3):
-#                 sys_inp = gr.Textbox(label="System Prompt (optional)")
-#                 inp = gr.Textbox(label="Prompt")
-#                 with gr.Row():
-#                     btn = gr.Button("Chat")
-#                     stop_btn = gr.Button("Stop")
-#                     clear_btn = gr.Button("Clear")
-
-#     chat_sub = inp.submit(check_rand, [rand, seed], seed).then(chat_inf,
-#                                                                [sys_inp, inp, chat_b, client_choice, seed, temp, tokens,
-#                                                                 top_p, rep_p], chat_b)
-#     go = btn.click(check_rand, [rand, seed], seed).then(chat_inf,
-#                                                         [sys_inp, inp, chat_b, client_choice, seed, temp, tokens, top_p,
-#                                                          rep_p], chat_b)
-#     stop_btn.click(None, None, None, cancels=[go, chat_sub])
-#     clear_btn.click(clear_fn, None, [chat_b])
-# app.queue(default_concurrency_limit=10).launch()
 import gradio as gr
-import pandas as pd
-import re
-import time
-import plotly.express as px
-
-class CSVFileUploader:
-    def __init__(self):
-        self.file = None
-        self.selected_column = None
-        self.selected_row = None
-
-    def upload_file(self, file):
-        self.file = pd.read_csv(file.name)
-        df = self.file
-
-        data_preview = df.head()
-        data_info = df.info()
-        descriptive_statistics = df.describe()
-
-        return data_preview, data_info, descriptive_statistics
-
-def main(file):
-    uploader = CSVFileUploader()
-    data_preview, data_info, descriptive_statistics = uploader.upload_file(file)
-    return data_preview, data_info, descriptive_statistics
-
-iface = gr.Interface(fn=main, 
-                      inputs="file",
-                      outputs=["text", "text", "text"],
-                      title="Gemma CSV Infer",
-                      description="Upload a CSV file to preview its data, view information, and descriptive statistics.")
-iface.launch(share=True)
-
-# -----------------------------------------------------------------------
-
+from huggingface_hub import InferenceClient
+import random
+
+models = [
+    "google/gemma-7b",
+    "google/gemma-7b-it",
+    "google/gemma-2b",
+    "google/gemma-2b-it"
+]
+
+clients = []
+for model in models:
+    clients.append(InferenceClient(model))
+
+
+def format_prompt(message, history):
+    prompt = ""
+    if history:
+        for user_prompt, bot_response in history:
+            prompt += f"<start_of_turn>user{user_prompt}<end_of_turn>"
+            prompt += f"<start_of_turn>model{bot_response}"
+    prompt += f"<start_of_turn>user{message}<end_of_turn><start_of_turn>model"
+    return prompt
+
+
+def chat_inf(system_prompt, prompt, history, client_choice, seed, temp, tokens, top_p, rep_p):
+    client = clients[int(client_choice) - 1]
+    if not history:
+        history = []
+        hist_len = 0
+    if history:
+        hist_len = len(history)
+        print(hist_len)
+
+    generate_kwargs = dict(
+        temperature=temp,
+        max_new_tokens=tokens,
+        top_p=top_p,
+        repetition_penalty=rep_p,
+        do_sample=True,
+        seed=seed,
+    )
+    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
+                                    return_full_text=False)
+    output = ""
+
+    for response in stream:
+        output += response.token.text
+        yield [(prompt, output)]
+    history.append((prompt, output))
+    yield history
+
+
+def clear_fn():
+    return None
+
+
+rand_val = random.randint(1, 1111111111111111)
+
+
+def check_rand(inp, val):
+    if inp is True:
+        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
+    else:
+        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))
+
+
+with gr.Blocks() as app:
+    gr.HTML(
+        """<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1></center>""")
+    with gr.Group():
+        with gr.Row():
+            client_choice = gr.Dropdown(label="Models", type='index', choices=[c for c in models], value=models[0],
+                                        interactive=True)
+    chat_b = gr.Chatbot(height=500)
+    with gr.Group():
+        with gr.Row():
+            with gr.Column(scale=1):
+                with gr.Group():
+                    rand = gr.Checkbox(label="Random Seed", value=True)
+                    seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
+                    tokens = gr.Slider(label="Max new tokens", value=6400, minimum=0, maximum=8000, step=64,
+                                       interactive=True, visible=True, info="The maximum number of tokens")
+            with gr.Column(scale=1):
+                with gr.Group():
+                    temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+                    top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+                    rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)
+
+    with gr.Group():
+        with gr.Row():
+            with gr.Column(scale=3):
+                sys_inp = gr.Textbox(label="System Prompt (optional)")
+                inp = gr.Textbox(label="Prompt")
+                with gr.Row():
+                    btn = gr.Button("Chat")
+                    stop_btn = gr.Button("Stop")
+                    clear_btn = gr.Button("Clear")
+
+    chat_sub = inp.submit(check_rand, [rand, seed], seed).then(chat_inf,
+                                                               [sys_inp, inp, chat_b, client_choice, seed, temp, tokens,
+                                                                top_p, rep_p], chat_b)
+    go = btn.click(check_rand, [rand, seed], seed).then(chat_inf,
+                                                        [sys_inp, inp, chat_b, client_choice, seed, temp, tokens, top_p,
+                                                         rep_p], chat_b)
+    stop_btn.click(None, None, None, cancels=[go, chat_sub])
+    clear_btn.click(clear_fn, None, [chat_b])
+app.queue(default_concurrency_limit=10).launch()