Create app.py

app.py

@@ -0,0 +1,213 @@
+import os
+import time
+from typing import List, Tuple, Optional, Dict
+
+import google.generativeai as genai
+import gradio as gr
+from PIL import Image
+
+print("google-generativeai:", genai.__version__)
+
+GGL_API_KEY = os.environ.get("GGL_API_KEY")
+gglusr = os.environ.get("GGL_USR")
+gglpwd = os.environ.get("GGL_PWD")
+
+TITLE = """<h2 align="center">🫐Blueberry-AI ✨Gemini Chat🫐</h2>"""
+IMAGE_WIDTH = 512
+
+
+def preprocess_stop_sequences(stop_sequences: str) -> Optional[List[str]]:
+    if not stop_sequences:
+        return None
+    return [sequence.strip() for sequence in stop_sequences.split(",")]
+
+
+def preprocess_image(image: Image.Image) -> Optional[Image.Image]:
+    image_height = int(image.height * IMAGE_WIDTH / image.width)
+    return image.resize((IMAGE_WIDTH, image_height))
+
+
+def preprocess_chat_history(
+    history: List[Tuple[Optional[str], Optional[str]]]
+) -> List[Dict[str, List[str]]]:
+    messages = []
+    for user_message, model_message in history:
+        if user_message is not None:
+            messages.append({'role': 'user', 'parts': [user_message]})
+        if model_message is not None:
+            messages.append({'role': 'model', 'parts': [model_message]})
+    return messages
+
+
+def user(text_prompt: str, chatbot: List[Tuple[str, str]]):
+    return "", chatbot + [[text_prompt, None]]
+
+
+def bot(
+    image_prompt: Optional[Image.Image],
+    temperature: float,
+    max_output_tokens: int,
+    stop_sequences: str,
+    top_k: int,
+    top_p: float,
+    chatbot: List[Tuple[str, str]]
+):
+    
+    text_prompt = chatbot[-1][0]
+    genai.configure(api_key=GGL_API_KEY)
+    generation_config = genai.types.GenerationConfig(
+        temperature=temperature,
+        max_output_tokens=max_output_tokens,
+        stop_sequences=preprocess_stop_sequences(stop_sequences=stop_sequences),
+        top_k=top_k,
+        top_p=top_p)
+
+    if image_prompt is None:
+        model = genai.GenerativeModel('gemini-pro')
+        response = model.generate_content(
+            preprocess_chat_history(chatbot),
+            stream=True,
+            generation_config=generation_config)
+        response.resolve()
+    else:
+        image_prompt = preprocess_image(image_prompt)
+        model = genai.GenerativeModel('gemini-pro-vision')
+        response = model.generate_content(
+            contents=[text_prompt, image_prompt],
+            stream=True,
+            generation_config=generation_config)
+        response.resolve()
+
+    # streaming effect
+    chatbot[-1][1] = ""
+    for chunk in response:
+        for i in range(0, len(chunk.text), 10):
+            section = chunk.text[i:i + 10]
+            chatbot[-1][1] += section
+            time.sleep(0.01)
+            yield chatbot
+
+
+image_prompt_component = gr.Image(type="pil", label="Image", scale=1, height=400)
+chatbot_component = gr.Chatbot(
+    label='Gemini',
+    bubble_full_width=False,
+    avatar_images=("./usr.png", "./bot.png"),
+    likeable=True, 
+    show_copy_button=True,
+    scale=2,
+    height=400
+)
+text_prompt_component = gr.Textbox(
+    placeholder="Hi there!",
+    label="Ask me anything and press Enter"
+)
+run_button_component = gr.Button()
+temperature_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.4,
+    step=0.05,
+    label="Temperature",
+    info=(
+        "Temperature controls the degree of randomness in token selection. Lower "
+        "temperatures are good for prompts that expect a true or correct response, "
+        "while higher temperatures can lead to more diverse or unexpected results. "
+    ))
+max_output_tokens_component = gr.Slider(
+    minimum=1,
+    maximum=2048,
+    value=1024,
+    step=1,
+    label="Token limit",
+    info=(
+        "Token limit determines the maximum amount of text output from one prompt. A "
+        "token is approximately four characters. The default value is 2048."
+    ))
+stop_sequences_component = gr.Textbox(
+    label="Add stop sequence",
+    value="",
+    type="text",
+    placeholder="STOP, END",
+    info=(
+        "A stop sequence is a series of characters (including spaces) that stops "
+        "response generation if the model encounters it. The sequence is not included "
+        "as part of the response. You can add up to five stop sequences."
+    ))
+top_k_component = gr.Slider(
+    minimum=1,
+    maximum=40,
+    value=32,
+    step=1,
+    label="Top-K",
+    info=(
+        "Top-k changes how the model selects tokens for output. A top-k of 1 means the "
+        "selected token is the most probable among all tokens in the model’s "
+        "vocabulary (also called greedy decoding), while a top-k of 3 means that the "
+        "next token is selected from among the 3 most probable tokens (using "
+        "temperature)."
+    ))
+top_p_component = gr.Slider(
+    minimum=0,
+    maximum=1,
+    value=1,
+    step=0.01,
+    label="Top-P",
+    info=(
+        "Top-p changes how the model selects tokens for output. Tokens are selected "
+        "from most probable to least until the sum of their probabilities equals the "
+        "top-p value. For example, if tokens A, B, and C have a probability of .3, .2, "
+        "and .1 and the top-p value is .5, then the model will select either A or B as "
+        "the next token (using temperature). "
+    ))
+
+user_inputs = [
+    text_prompt_component,
+    chatbot_component
+]
+
+bot_inputs = [
+    image_prompt_component,
+    temperature_component,
+    max_output_tokens_component,
+    stop_sequences_component,
+    top_k_component,
+    top_p_component,
+    chatbot_component
+]
+
+with gr.Blocks() as demo:
+    gr.HTML(TITLE)
+    with gr.Column():
+        with gr.Row():
+            image_prompt_component.render()
+            chatbot_component.render()
+        text_prompt_component.render()
+        run_button_component.render()
+        with gr.Accordion("Parameters", open=False):
+            temperature_component.render()
+            max_output_tokens_component.render()
+            stop_sequences_component.render()
+            with gr.Accordion("Advanced", open=False):
+                top_k_component.render()
+                top_p_component.render()
+
+    run_button_component.click(
+        fn=user,
+        inputs=user_inputs,
+        outputs=[text_prompt_component, chatbot_component],
+        queue=False
+    ).then(
+        fn=bot, inputs=bot_inputs, outputs=[chatbot_component],
+    )
+
+    text_prompt_component.submit(
+        fn=user,
+        inputs=user_inputs,
+        outputs=[text_prompt_component, chatbot_component],
+        queue=False
+    ).then(
+        fn=bot, inputs=bot_inputs, outputs=[chatbot_component],
+    )
+
+demo.queue(max_size=99).launch(auth=(gglusr, gglpwd),show_api=False, debug=False, show_error=True)