Update app.py

app.py

@@ -1,30 +1,36 @@
 import gradio as gr
 
-# Define custom CSS for a pastel gradient
+# Custom CSS for pastel gradient
 css = """
 .gradio-container {
     background: linear-gradient(to right, #FFDEE9, #B5FFFC);
 }
 """
 
-def respond(message):
-    # Simple example function returning a response
-    return f"Vous avez dit: {message}"
+# Load the Mistral-7B-Instruct-v0.3 model via Gradio's load function
+model = gr.load("models/mistralai/Mistral-7B-Instruct-v0.3")
+
+def inference_fn(prompt):
+    """
+    This function calls the loaded model with the user's prompt.
+    gr.load(...) returns a Gradio interface object, so we can call it like a function.
+    """
+    # If the loaded model is a pipeline or interface, calling it directly returns the response.
+    response = model(prompt)
+    return response
 
 with gr.Blocks(css=css) as demo:
-    # Display the greeting message
-    gr.Markdown(
-        "<h1 style='text-align: center;'>Bonjour Dans le chat du consentement</h1>"
-    )
+    # Greeting at the top
+    gr.Markdown("<h1 style='text-align: center;'>Bonjour Dans le chat du consentement</h1>")
 
-    # Create a simple text input and output
+    # Create the input/output layout
     with gr.Row():
-        user_input = gr.Textbox(label="Entrez votre message ici")
-        btn = gr.Button("Envoyer")
-    output = gr.Textbox(label="Réponse")
+        user_input = gr.Textbox(label="Entrez votre message ici:", lines=3)
+        output = gr.Textbox(label="Réponse du Modèle Mistral-7B-Instruct:", lines=5)
+    send_button = gr.Button("Envoyer")
 
-    # Link the button to the respond function
-    btn.click(fn=respond, inputs=user_input, outputs=output)
+    # Link the button to inference_fn
+    send_button.click(fn=inference_fn, inputs=user_input, outputs=output)
 
 # Launch the app
 if __name__ == "__main__":