Upload app.py

app.py

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+# Para Spaces de Hugging Face, la app principal debe llamarse app.py
+# Este archivo es una copia de ejemplo_2_.py
+
+import streamlit as st
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSeq2SeqLM,
+    AutoModelForSequenceClassification,
+    pipeline
+)
+import torch
+
+st.set_page_config(page_title="SLM Demo: QA + Sentiment", page_icon="🧠")
+st.title("🧠 Small Language Models Demo")
+st.markdown("""
+Esta app compara tres Small Language Models:
+- `flan-t5-small` para responder preguntas.
+- `distilBERT` para análisis de sentimiento.
+- `distilBERT` fine-tuned para análisis de sentimiento.
+""")
+
+# Cargar modelos y tokenizers
+@st.cache_resource
+def load_models():
+    # FLAN-T5 para QA
+    flan_name = "google/flan-t5-small"
+    flan_tokenizer = AutoTokenizer.from_pretrained(flan_name)
+    flan_model = AutoModelForSeq2SeqLM.from_pretrained(flan_name)
+
+    # DistilBERT para clasificación (modelo base)
+    distil_name = "distilbert-base-uncased-finetuned-sst-2-english"
+    sentiment_analyzer = pipeline("sentiment-analysis", model=distil_name)
+
+    # DistilBERT fine-tuned propio
+    custom_name = "juancmamacias/jd-jcms"
+    custom_analyzer = pipeline("sentiment-analysis", model=custom_name)
+
+    return flan_tokenizer, flan_model, sentiment_analyzer, custom_analyzer
+
+flan_tokenizer, flan_model, sentiment_analyzer, custom_analyzer = load_models()
+
+# Historial de preguntas
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# Entrada del usuario
+question = st.text_input("💬 Escribe una pregunta o frase para analizar:")
+
+if st.button("Procesar") and question:
+    with st.spinner("Procesando..."):
+
+        # ➤ Respuesta con FLAN-T5
+        input_ids = flan_tokenizer(question, return_tensors="pt").input_ids
+        outputs = flan_model.generate(input_ids, max_length=50)
+        flan_answer = flan_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # ➤ Clasificación con DistilBERT base
+        sentiment = sentiment_analyzer(question)[0]
+        sentiment_label = sentiment['label']
+        sentiment_score = round(sentiment['score'], 3)
+
+        # ➤ Clasificación con DistilBERT fine-tuned propio
+        custom_sentiment = custom_analyzer(question)[0]
+        custom_label = custom_sentiment['label']
+        custom_score = round(custom_sentiment['score'], 3)
+
+        # Guardar en historial
+        st.session_state.history.append({
+            "question": question,
+            "answer": flan_answer,
+            "sentiment": f"{sentiment_label} ({sentiment_score})",
+            "custom_sentiment": f"{custom_label} ({custom_score})"
+        })
+
+# Mostrar historial
+if st.session_state.history:
+    st.markdown("### 📜 Historial")
+    for i, item in enumerate(reversed(st.session_state.history), 1):
+        st.markdown(f"""
+        **{i}. Entrada:** {item['question']}  
+        🧠 **Respuesta (FLAN):** {item['answer']}  
+        ❤️ **Sentimiento (base):** {item['sentiment']}  
+        💙 **Sentimiento (propio):** {item['custom_sentiment']}  
+        ---""")