Create app.py

app.py

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+#MisterAI/Docker_Ollama
+#app.py_02
+#https://huggingface.co/spaces/MisterAI/Docker_Ollama/
+
+import logging
+import requests
+from pydantic import BaseModel
+from langchain_community.llms import Ollama
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+import gradio as gr
+import threading
+import subprocess
+from bs4 import BeautifulSoup
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Cache pour stocker les modèles déjà chargés
+loaded_models = {}
+
+# Variable pour suivre l'état du bouton "Stop"
+stop_flag = False
+
+def get_model_list():
+    url = "https://ollama.com/search"
+    response = requests.get(url)
+
+    # Vérifier si la requête a réussi
+    if response.status_code == 200:
+        # Utiliser BeautifulSoup pour analyser le HTML
+        soup = BeautifulSoup(response.text, 'html.parser')
+        model_list = []
+
+        # Trouver tous les éléments de modèle
+        model_elements = soup.find_all('li', {'x-test-model': True})
+
+        for model_element in model_elements:
+            model_name = model_element.find('span', {'x-test-search-response-title': True}).text.strip()
+            size_elements = model_element.find_all('span', {'x-test-size': True})
+
+            # Filtrer les modèles par taille
+            for size_element in size_elements:
+                size = size_element.text.strip()
+                if size.endswith('m'):
+                    # Tous les modèles en millions sont acceptés
+                    model_list.append(f"{model_name}:{size}")
+                elif size.endswith('b'):
+                    # Convertir les modèles en milliards en milliards
+                    size_value = float(size[:-1])
+                    if size_value <= 10:  # Filtrer les modèles <= 10 milliards de paramètres
+                        model_list.append(f"{model_name}:{size}")
+
+        return model_list
+    else:
+        logger.error(f"Erreur lors de la récupération de la liste des modèles : {response.status_code} - {response.text}")
+        return []
+
+def get_llm(model_name):
+    callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+    return Ollama(model=model_name, callback_manager=callback_manager)
+
+class InputData(BaseModel):
+    model_name: str
+    input: str
+    max_tokens: int = 256
+    temperature: float = 0.7
+
+def pull_model(model_name):
+    try:
+        # Exécuter la commande pour tirer le modèle
+        subprocess.run(["ollama", "pull", model_name], check=True)
+        logger.info(f"Model {model_name} pulled successfully.")
+    except subprocess.CalledProcessError as e:
+        logger.error(f"Failed to pull model {model_name}: {e}")
+        raise
+
+def check_and_load_model(model_name):
+    # Vérifier si le modèle est déjà chargé
+    if model_name in loaded_models:
+        logger.info(f"Model {model_name} is already loaded.")
+        return loaded_models[model_name]
+    else:
+        logger.info(f"Loading model {model_name}...")
+        # Tirer le modèle si nécessaire
+        pull_model(model_name)
+        llm = get_llm(model_name)
+        loaded_models[model_name] = llm
+        return llm
+
+# Interface Gradio
+def gradio_interface(model_name, input, max_tokens, temperature, stop_button=None):
+    global stop_flag
+    stop_flag = False
+    response = None  # Initialisez la variable response ici
+
+    def worker():
+        nonlocal response  # Utilisez nonlocal pour accéder à la variable response définie dans la fonction parente
+        llm = check_and_load_model(model_name)
+        response = llm(input, max_tokens=max_tokens, temperature=temperature)
+
+    thread = threading.Thread(target=worker)
+    thread.start()
+    thread.join()
+
+    if stop_flag:
+        return "Processing stopped by the user."
+    else:
+        return response  # Maintenant, response est accessible ici
+
+model_list = get_model_list()
+
+demo = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Dropdown(model_list, label="Select Model", value="mistral:7b"),
+        gr.Textbox(label="Input"),
+        gr.Slider(minimum=1, maximum=2048, step=1, label="Max Tokens", value=256),
+        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="Temperature", value=0.7),
+        gr.Button(value="Stop", variant="stop")
+    ],
+    outputs=[
+        gr.Textbox(label="Output")
+    ],
+    title="Ollama Demo"
+)
+
+def stop_processing():
+    global stop_flag
+    stop_flag = True
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+