Update app.py

app.py

@@ -1,30 +1,26 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import pipeline
 
-# Load the text generation model
-generator = InferenceClient("mistralai/Codestral-22B-v0.1")
+# Load the text generation pipeline
+pipe = pipeline("text-generation", model="mistralai/Codestral-22B-v0.1")
 
 # Define the function for text generation
 def generate(text):
     try:
-        # Generate text using the correct API call
-        result = generator.text_generation(text, details=True, stream=False)
-        # Extract and return the generated text
-        if "generated_text" in result:
-            return result["generated_text"]
-        elif isinstance(result, list) and len(result) > 0:
-            return result[0]  # If the response is a list, return the first item
-        else:
-            return "Error: Unable to generate text."
+        # Format the input and generate text
+        messages = [{"role": "user", "content": text}]
+        result = pipe(messages, max_length=100, num_return_sequences=1)
+        # Extract the generated text
+        return result[0]['generated_text'] if result else "Error: No text generated."
     except Exception as e:
         return f"Error: {e}"
 
 # Define examples
 examples = [
-    ["What is the fundamental difference between supervised and unsupervised learning"],
-    ["What is overfitting in supervised learning"],
-    ["What is a convolutional neural network"],
-    ["Describe the concept of transfer learning and its significance in deep learning"]
+    ["Who are you?"],
+    ["Explain the difference between AI and ML."],
+    ["What is overfitting in machine learning?"],
+    ["Describe transfer learning and its importance."]
 ]
 
 # Create the Gradio interface