Update app.py

app.py

@@ -1,38 +1,32 @@
-import torch
-from transformers import pipeline
-
-#pipe = pipeline("text-generation", "meta-llama/Meta-Llama-3-8B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
-pipe = pipeline("text-generation", "mistralai/Mistral-7B-Instruct-v0.3", torch_dtype=torch.bfloat16, device_map="auto")
-#response = pipe(chat, max_new_tokens=512)
-
-
 import gradio as gr
+import torch
 
+# Charger un modèle de Question-Answering
+from huggingface_hub import InferenceClient
+model = InferenceClient(base_url='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud')
 
+def answer_question(context, question):
+    if not context.strip() or not question.strip():
+        return "Veuillez fournir un contexte et une question."
 
-# Fonction de prédiction
-def predict(question, context):
     chat = [
-        {"role": "system", "content": context}
-    ]
-    chat.append({"role": "user", "content": question})
-    response = pipe(chat, max_new_tokens=512)
-    answer = response[0]['generated_text']
-    
-    return f"{answer}"
-
+      {"role": "system", "content": context},
+      {"role": "user", "content": question}
+  ]
+    response = model.chat_completion(chat, max_tokens=512)
+    return response['choices'][0]['message']['content']
 
 # Interface Gradio
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Textbox(label="Entrez votre question"),
-        gr.Textbox(label="Entrez le contexte")
-    ],
-    outputs=gr.Textbox(label="Réponse du modèle")
-)
+with gr.Blocks() as demo:
+    gr.Markdown("## Chatbot avec Contexte et Question")
+
+    context_input = gr.Textbox(label="Contexte", placeholder="Entrez un contexte ici...")
+    question_input = gr.Textbox(label="Question", placeholder="Posez une question sur le contexte...")
+    output = gr.Textbox(label="Réponse", interactive=False)
+
+    submit_button = gr.Button("Obtenir la Réponse")
 
+    submit_button.click(answer_question, inputs=[context_input, question_input], outputs=output)
 
 # Lancer l'interface
-if __name__ == "__main__":
-    iface.launch()
+demo.launch()