Create app.py

app.py

@@ -1,64 +1,31 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    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
-        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)",
-        ),
-    ],
-)
-
-
+import os
+import requests
+from telegram import Update
+from telegram.ext import Application, MessageHandler, filters, CallbackContext
+
+# 🔹 Configuration avec des variables d'environnement
+TELEGRAM_TOKEN = os.getenv("TELEGRAM_TOKEN")
+HUGGINGFACE_MODEL = "mistralai/Mistral-7B-Instruct"
+
+# 🔹 Fonction pour appeler le modèle Hugging Face
+def get_ai_response(user_input):
+    url = f"https://api-inference.huggingface.co/models/{HUGGINGFACE_MODEL}"
+    headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
+    data = {"inputs": user_input}
+    
+    response = requests.post(url, json=data, headers=headers)
+    return response.json()[0]["generated_text"] if "generated_text" in response.json()[0] else "Je ne comprends pas."
+
+# 🔹 Fonction pour gérer les messages Telegram
+async def handle_message(update: Update, context: CallbackContext):
+    user_message = update.message.text
+    bot_response = get_ai_response(user_message)
+    await update.message.reply_text(bot_response)
+
+# 🔹 Configuration du bot Telegram
+app = Application.builder().token(TELEGRAM_TOKEN).build()
+app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))
+
+# 🔹 Lancer le bot
 if __name__ == "__main__":
-    demo.launch()
+    app.run_polling()