Update app.py

app.py

@@ -1,15 +1,23 @@
 import gradio as gr
-from transformers import pipeline
 
 # Load the text-generation pipeline with Mistral model
-pipe = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3")
+from langchain_huggingface import HuggingFaceEndpoint
 
+
+# Initialize the LLM and other components
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    task="text-generation",
+    max_new_tokens=4096,
+    temperature=0.5,
+    do_sample=False,
+)
 # Define the function to process user input
 def classify_text(text):
     prompt = "Classify the following text into a category or topic:"
     input_text = f"{prompt}\n{text}"
-    results = pipe(input_text, max_length=100, num_return_sequences=1)
-    return results[0]['generated_text']
+    results = llm.invoke(input_text)
+    return results
 
 # Create Gradio interface
 interface = gr.Interface(