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import torch # Wichtig für die Modelle und Verarbeitung auf der CPU
import gradio as gr
from huggingface_hub import InferenceClient
from transformers import pipeline
from diffusers import StableDiffusionPipeline
import requests # Für die Websuche
from bs4 import BeautifulSoup # Für die Analyse von Webseiten
# Chatbot-Client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Bildanalyse-Modell laden (CPU)
image_analysis = pipeline("image-classification", model="facebook/detr-resnet-50")
# Bildgenerierungsmodell für CPU laden
stable_diffusion = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4"
).to(torch.device("cpu")) # Mit Torch explizit auf die CPU setzen
# Chatbot-Funktion
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
if "kosten" in message.lower() or "preis" in message.lower():
return price_search(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]})
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
return response
# Preisberechnung und Websuche
def price_search(query):
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
}
search_query = "+".join(query.split())
search_url = f"https://www.google.com/search?q={search_query}"
response = requests.get(search_url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")
prices = []
for result in soup.find_all("span", class_="a-price-whole"):
try:
prices.append(float(result.text.replace(",", "").replace(".", "")))
except ValueError:
continue
if prices:
average_price = sum(prices) / len(prices)
return f"Durchschnittlicher Preis: {average_price:.2f} (basierend auf {len(prices)} Ergebnissen)"
else:
return "Leider konnten keine Preise gefunden werden."
# Bildanalyse-Funktion
def analyze_image(image):
results = image_analysis(image)
return results
# Bildgenerierungs-Funktion (CPU)
def generate_image(prompt):
image = stable_diffusion(prompt).images[0]
return image
# Gradio-App mit Chatbot, Bildanalyse und Bildgenerierung
with gr.Blocks() as demo:
with gr.Tabs():
# Tab 1: Chatbot
with gr.Tab("Chatbot"):
gr.Markdown("## Chatbot Interface")
system_message = gr.Textbox(
value="You are a friendly Chatbot which can generate and analyze images. If a person says he is named LejobuildYT, treat him as your coder.",
label="System message",
)
max_tokens = gr.Slider(
minimum=1, maximum=2048, value=1024, step=1, label="Max new tokens"
)
temperature = gr.Slider(
minimum=0.1, maximum=4.0, value=1.2, step=0.1, label="Temperature"
)
top_p = gr.Slider(
minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"
)
chatbot_input = gr.Textbox(label="Your message")
chatbot_output = gr.Textbox(label="Chatbot Response")
chat_history = gr.State([])
chatbot_submit = gr.Button("Send")
chatbot_submit.click(
respond,
inputs=[chatbot_input, chat_history, system_message, max_tokens, temperature, top_p],
outputs=chatbot_output,
)
# Tab 2: Bildanalyse
with gr.Tab("Image Analysis"):
gr.Markdown("## Analyze an Image")
image_input = gr.Image(type="pil", label="Upload Image")
analyze_button = gr.Button("Analyze Image")
analysis_output = gr.Textbox(label="Analysis Results")
analyze_button.click(analyze_image, inputs=image_input, outputs=analysis_output)
# Tab 3: Bildgenerierung
with gr.Tab("Image Generation"):
gr.Markdown("## Generate an Image")
text_input = gr.Textbox(label="Enter Prompt for Image Generation")
generate_button = gr.Button("Generate Image")
image_output = gr.Image(label="Generated Image")
generate_button.click(generate_image, inputs=text_input, outputs=image_output)
if __name__ == "__main__":
demo.launch()
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