Readme and add file to bot

README.md

@@ -10,4 +10,125 @@ pinned: false
 license: mit
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+### Choisir la langue : [Français](#français) | [English](#english)
+
+## Français
+
+<a name="français"></a>
+
+## Description
+
+Gaia Chat est un exemple de chatbot utilisant [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), et l'[API d'inférence de Hugging Face](https://huggingface.co/docs/api-inference/index). Ce projet montre comment créer un chatbot interactif en utilisant ces outils.
+
+## Prérequis
+
+- Python 3.6 ou supérieur
+- Gradio
+- Chardet
+
+
+## Installation
+
+### 1. Installer Python
+
+Assurez-vous que Python est installé. 
+
+```sh
+python --version
+```
+
+1. Rendez-vous sur le site officiel de Python : [https://www.python.org/downloads/](https://www.python.org/downloads/)
+2. Vérifiez l’installation avec la commande :
+   ```sh
+   python --version
+   ```
+
+
+
+### 2. Installer les dépendances
+
+Installez `Gradio` :
+
+```sh
+pip install gradio 
+```
+
+Installez `Chardet` :
+
+```sh
+pip install chardet 
+```
+### 5. Gener un Access token sur Hugging Face
+
+Rendez-vous sur votre profil Hugging Face pour genenez votre Access Token : [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+
+### 4. Lancer l'application
+
+Exécutez la commande suivante pour démarrer Gaia Chat :
+
+```sh
+python app.py
+```
+
+Une fois l'application lancée, ouvrez votre navigateur et accédez à l'URL indiquée dans le terminal pour interagir avec le chatbot.
+
+
+---
+
+
+## English
+
+<a name="english"></a>
+
+
+## Description
+
+Gaia Chat is an example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and l '[Hugging Face Inference API](https://huggingface.co/docs/api-inference/index). This project shows how to create an interactive chatbot using these tools.
+
+
+## prerequisites
+
+- Python 3.6
+- Gradio
+- Chardet
+
+## Facilities
+
+### 1. Python install
+
+Make sure Python is installed.
+
+```sh
+python --version
+```
+
+1. Go to the official Python website: [https://www.python.org/downloads/](https://www.python.org/downloads/)
+2. Verify the installation with the command:
+```sh
+   python --version
+```
+
+### 2. Install dependencies
+
+Install `Gradio` :
+```sh
+pip install gradio 
+```
+
+Install `Chardet`
+```sh
+pip install chardet 
+```
+### 5. Generate Access Token on Hugging Face
+
+Go to your Hugging Face profile to generate your access token: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+
+### 4. Launch the application
+
+Run the following command to start Gaia Chat:
+
+```sh
+python app.py
+```
+
+Once the application is launched, open your browser and navigate to the URL given in the terminal to interact with the chatbot.

app.py

@@ -1,32 +1,38 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import chardet
 
-"""
-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")
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token="") #generate Access tokens
 
+file_content = None
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def respond(message, history, system_message, max_tokens, temperature, top_p, file=None):
+    global file_content
 
+    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 = ""
 
+    if file:
+        try:
+            file_content = file.decode("utf-8")  
+        except UnicodeDecodeError:
+            result = chardet.detect(file)
+            encoding = result['encoding']
+            file_content = file.decode(encoding, errors='ignore')
+
+    if "contenu du fichier" in message.lower() and file_content:
+        response += f"Contenu du fichier :\n{file_content}"
+        yield response
+        return
+
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -35,30 +41,21 @@ 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)",
-        ),
-    ],
-)
-
+with gr.Blocks() as demo:
+    gr.Image(value="logo-gaia.png", label="Logo")
+    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)"),
+            gr.File(label="Télécharger un fichier", type="binary"),  
+        ],
+    )
 
 if __name__ == "__main__":
     demo.launch()