Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,130 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain_community.embeddings import OpenAIEmbeddings
+from langchain.chat_models import ChatOpenAI
+from langchain_community.vectorstores import Pinecone as LangchainPinecone
+from PIL import Image
+import os
 
-"""
-# Welcome to Streamlit!
+# ========================
+# Configuración de claves
+# ========================
+openai_api_key = os.environ.get("OPENAI_API_KEY")
+pinecone_key = os.environ.get("PINECONE_API_KEY")
+pinecone_env = "us-east-1"
+index_name = "knowledge-base-eliminatorias"
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# =====================
+# Configuración de app
+# =====================
+st.set_page_config(page_title="Chatbot usando ChatGPT", page_icon="⚽")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+# ====================
+# Mensaje de bienvenida
+# ====================
+msg_chatbot = """
+Soy un chatbot que te ayudará a conocer sobre las eliminatorias sudamericanas: 
+### Puedo ayudarte con las siguientes preguntas:
+- ¿Quién es el líder en la tabla de posiciones?
+- ¿Cuáles son los próximos partidos de Perú?
+- Bríndame la tabla de posiciones
+- Y muchas más...
 """
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# ======================
+# Reinicio de historial (antes de dibujar mensajes)
+# ======================
+if st.session_state.get("clear_chat", False):
+    st.session_state.clear_chat = False
+    st.session_state.messages = [{"role": "assistant", "content": msg_chatbot}]
+    st.rerun()
+
+# =========
+# Sidebar
+# =========
+with st.sidebar:
+    st.title("Chatbot usando OpenAI (ChatGPT)")
+    image = Image.open('src/conmebol.jpg')
+    st.image(image, caption='Conmebol')
+    st.markdown("""
+        ### Propósito
+        Este chatbot utiliza una base de conocimiento (Pinecone) con información del sitio web de Marca.
+        Usa Langchain con ChatGPT de OpenAI.
+        ### Fuentes 
+        - Marca - (https://www.marca.com)
+    """)
+
+    if st.button("🧹 Limpiar chat"):
+        st.session_state.clear_chat = True
+        st.rerun()
+
+# =======================
+# Inicializar historial si es necesario
+# =======================
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role": "assistant", "content": msg_chatbot}]
+
+# =======================
+# Mostrar historial
+# =======================
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# ====================
+# Generar respuesta
+# ====================
+def generate_openai_pinecone_response(prompt_input):
+    llm = ChatOpenAI(
+        openai_api_key=openai_api_key,
+        model_name="gpt-3.5-turbo",
+        temperature=0.85
+    )
+
+    template = """Responde a la pregunta basada en el siguiente contexto.
+    Si no puedes responder, di: "No lo sé, disculpa, puedes buscar en internet."
+    Contexto:
+    {context}
+    Pregunta: {question}
+    Respuesta usando también emoticones:
+    """
+
+    prompt = PromptTemplate(
+        input_variables=["context", "question"],
+        template=template
+    )
+
+    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
+
+    vectorstore = LangchainPinecone.from_existing_index(
+        index_name=index_name,
+        embedding=embeddings
+    )
+
+    qa = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type='stuff',
+        retriever=vectorstore.as_retriever(),
+        verbose=True,
+        chain_type_kwargs={"prompt": prompt}
+    )
+
+    return qa.run(prompt_input)
+
+# ====================
+# Interfaz principal
+# ====================
+prompt = st.chat_input("Ingresa tu pregunta")
+if prompt:
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+
+if st.session_state.messages[-1]["role"] != "assistant":
+    with st.chat_message("assistant"):
+        with st.spinner("Esperando respuesta..."):
+            response = generate_openai_pinecone_response(prompt)
+            placeholder = st.empty()
+            placeholder.markdown(response)
+            st.session_state.messages.append({"role": "assistant", "content": response})