Create app.py

app.py

@@ -0,0 +1,45 @@
+import os
+from dotenv import load_dotenv
+import streamlit as st
+from transformers import AutoTokenizer
+import requests
+
+load_dotenv()
+
+modelos = {
+    "mistralai/Mixtral-8x7B-Instruct-v0.1": "[/INST]",
+    "google/gemma-7b-it": "<start_of_turn>model\n",
+}
+
+
+modelo = st.selectbox("Selecion um modelo: ", options=modelos)
+token_modelo = modelos[modelo]
+
+
+if "modelo_atual" not in st.session_state or st.session_state["modelo_atual"] != modelo:
+    st.session_state["modelo_atual"] = modelo
+    st.session_state["mensagens"] = []
+
+nome_modelo = st.session_state["modelo_atual"]
+tokenizer = AutoTokenizer.from_pretrained(nome_modelo, token=os.getenv("HF_KEY"))
+url = f"https://api-inference.huggingface.co/models/{nome_modelo}"
+headers = {"Authorization": f"Bearer {os.getenv('HF_KEY')}"}
+
+mensagens = st.session_state["mensagens"]
+area_chat = st.empty()
+
+pergunta_usuario = st.chat_input("Faça a sua pergunta aqui:")
+
+if pergunta_usuario:
+    mensagens.append({"role": "user", "content": pergunta_usuario})
+    template = tokenizer.apply_chat_template(
+        mensagens, tokenize=False, add_generation_prompt=True
+    )
+    json = {
+        "inputs": template,
+        "parameters": {"max_new_tokens": 1000},
+        "options": {"use_cache": False, "wait_for_model": True},
+    }
+
+    res = requests.post(url, json=json, headers=headers).json()
+    mensagem_chat = res[0]["generated_text"].split(token_modelo)[-1]