Update app.py

app.py

@@ -1,47 +1,61 @@
 import streamlit as st
 from wordcloud import WordCloud
-import numpy as np
 import matplotlib.pyplot as plt
-from plotly.subplots import make_subplots
-import plotly.graph_objects as go
-
-st.title("Nube de palabras 3D")
-
-text = st.text_area("Introduce hasta 20 palabras separadas por espacio:", "Ejemplo:Python es genial.")
-
-words = text.strip().split()[:20]
-wordcloud = WordCloud(width=800, height=400, random_state=1).generate(" ".join(words))
-
-fig = make_subplots(rows=1, cols=1, specs=[[{"type": "scene"}]])
-
-x, y = np.random.rand(500), np.random.rand(500)
-colors = np.array([(i/len(words), i/len(words)/2, 1.) for i in range(len(words))])
-
-scatter = go.Scatter3d(
-    x=x, y=y, z=np.zeros_like(x), 
-    mode='markers', 
-    marker={
-        'size': 12,
-        'color': colors,               
-        'opacity': 0.9,
-        'line': {'width': 2, 'color': 'black'}
-    },
-    name="Palabras",
-    text=words,
-    hoverinfo='text'
-)
-
-fig.add_trace(scatter)  
-fig.update_layout(
-    scene=dict(
-        xaxis_title='X-Axis',
-        yaxis_title='Y-Axis',
-        zaxis_title='Z-Axis',
-        aspectmode='data'
-    ),
-    title="Nube de palabras 3D",
-    width=700, height=600,
-    showlegend=False
-)
-
-st.write(fig, plugins=["sankey3d"])
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
+import networkx as nx
+import numpy as np
+from huggingface_hub import InferenceClient
+
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+def format_prompt(message, history):
+    prompt = "<s>"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def generate_wordcloud(text):
+    wordcloud = WordCloud(stopwords=ENGLISH_STOP_WORDS, background_color='white', width=800, height=400, max_words=20).generate(text)
+
+    fig = plt.figure(figsize=(10, 5))
+    ax = fig.add_subplot(111, projection='3d')
+
+    frequencies = wordcloud.words_
+
+    words = list(frequencies.keys())
+    sizes = list(frequencies.values())
+    colors = [hash(word) % 100 for word in words]
+
+    ax.scatter(words, sizes, colors, marker='o', s=sizes, depthshade=True)
+
+    ax.set_xlabel('Palabra')
+    ax.set_ylabel('Frecuencia')
+    ax.set_zlabel('Color')
+
+    ax.set_title('Nube de Palabras en 3D')
+
+    # Conectar palabras con líneas
+    G = nx.Graph()
+    for i, word in enumerate(words):
+        G.add_node(word)
+        for j in range(i + 1, len(words)):
+            G.add_edge(word, words[j])
+
+    pos = {word: (sizes[i], colors[i]) for i, word in enumerate(words)}
+    nx.draw(G, pos, ax=ax, with_labels=True, font_weight='bold', node_size=sizes, node_color=colors, font_size=8, edge_color='gray', alpha=0.5)
+
+    st.pyplot(fig)
+
+def main():
+    st.title('Generador de Nube de Palabras en 3D')
+
+    input_text = st.text_area('Ingrese el texto:', height=200)
+
+    if st.button('Generar Nube de Palabras'):
+        generate_wordcloud(input_text)
+
+if __name__ == '__main__':
+    main()