Update app.py

app.py

@@ -1,12 +1,23 @@
+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
 
-"""
-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
-"""
+# 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]],
@@ -15,8 +26,10 @@ def respond(
     temperature,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    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]})
@@ -24,9 +37,7 @@ def respond(
             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,
@@ -35,30 +46,90 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-
         response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-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)",
-        ),
-    ],
-)
+    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()