Update app.py

app.py

@@ -1,28 +1,27 @@
-import gradio as gr
-from bertopic import BERTopic
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import streamlit as st
 
-model_name = "RichieBurundi/Ariginalmodel"
+# Загрузка модели и токенизатора
+model_name = "Richieburundi/Ariginalmodel"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-try:
-    model = BERTopic.load(model_name)
-except Exception as e:
-    print(f"Error loading model: {e}")
-    # Здесь можно добавить fallback логику или использовать другую модель
+def generate_text(input_text, max_length=50):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=max_length, num_return_sequences=1, do_sample=True, top_k=50, top_p=0.95, num_beams=5)
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
 
-def generate_text(input_text):
-    try:
-        topics, probs = model.transform([input_text])
-        generated_text = model.generate_topic_labels(topics[0], probs[0], top_n=1)[0]
-        return generated_text
-    except Exception as e:
-        return f"Error generating text: {e}"
+st.title("AI Text Generation")
+st.write("Enter some text, and the AI will generate a response.")
 
-iface = gr.Interface(
-    fn=generate_text,
-    inputs="text",
-    outputs="text",
-    title="Ariginal Model Text Generation",
-    description="Enter your text, and the model will generate a response."
-)
+input_text = st.text_area("Input Text", height=200)
 
-iface.launch()
+if st.button("Generate Text"):
+    try:
+        generated_text = generate_text(input_text)
+        st.write("Generated Text:")
+        st.write(generated_text)
+    except Exception as e:
+        st.error(f"Error generating text: {e}")