Update app.py

app.py

@@ -1,104 +1,104 @@
-# 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
-
-@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
-
-
-
-# Cargar modelos antes de cualquier uso
-st.set_page_config(page_title="SLM Demo: QA + Sentiment", page_icon="🧠", layout="wide")
-
-# Ocultar header, footer y menú de Streamlit
-hide_streamlit_style = """
-    <style>
-    #MainMenu {visibility: hidden;}
-    header {visibility: hidden;}
-    footer {visibility: hidden;}
-    </style>
-"""
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-flan_tokenizer, flan_model, sentiment_analyzer, custom_analyzer = load_models()
-
-# Layout con dos columnas
-col1, col2 = st.columns([1,2])
-
-
-with col1:
-    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.
-    """)
-    st.markdown("""
-    ## Autores
-    - Juan Domingo ([GitHub](https://github.com/jdomdev))
-    - Juan Carlos Macías ([GitHub](https://github.com/juancmacias))
-    """)
-
-with col2:
-    if "history" not in st.session_state:
-        st.session_state.history = []
-
-    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 usando prompt explícito
-            prompt = f"Answer the following question: {question}"
-            input_ids = flan_tokenizer(prompt, 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)
-
-            # Traducción de etiquetas para ambos modelos
-            label_map = {"LABEL_0": "NEGATIVO", "LABEL_1": "POSITIVO", "NEGATIVE": "NEGATIVO", "POSITIVE": "POSITIVO"}
-            sentiment_label = label_map.get(sentiment_label, sentiment_label)
-
-            # ➤ Clasificación con DistilBERT fine-tuned propio
-            custom_sentiment = custom_analyzer(question)[0]
-            custom_label = custom_sentiment['label']
-            custom_score = round(custom_sentiment['score'], 3)
-            custom_label = label_map.get(custom_label, custom_label)
-
-            # 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']}  
-            ---""")
+# 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
+
+@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
+
+
+
+# Cargar modelos antes de cualquier uso
+st.set_page_config(page_title="SLM Demo: QA + Sentiment", page_icon="🧠", layout="wide")
+
+# Ocultar header, footer y menú de Streamlit
+hide_streamlit_style = """
+    <style>
+    #MainMenu {visibility: hidden;}
+    header {visibility: hidden;}
+    footer {visibility: hidden;}
+    </style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+flan_tokenizer, flan_model, sentiment_analyzer, custom_analyzer = load_models()
+
+# Layout con dos columnas
+col1, col2 = st.columns([1,2])
+
+
+with col1:
+    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.
+    """)
+    st.markdown("""
+    ## Autores
+    - Juan Domingo ([GitHub](https://github.com/jdomdev))
+    - Juan Carlos Macías ([GitHub](https://github.com/juancmacias))([Porfolio](https://www.juancarlosmacias.es))
+    """)
+
+with col2:
+    if "history" not in st.session_state:
+        st.session_state.history = []
+
+    question = st.text_input("💬 Escribe una pregunta o frase para analizar: improve in English")
+
+    if st.button("Procesar") and question:
+        with st.spinner("Procesando..."):
+            # ➤ Respuesta con FLAN-T5 usando prompt explícito
+            prompt = f"Answer the following question: {question}"
+            input_ids = flan_tokenizer(prompt, 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)
+
+            # Traducción de etiquetas para ambos modelos
+            label_map = {"LABEL_0": "NEGATIVO", "LABEL_1": "POSITIVO", "NEGATIVE": "NEGATIVO", "POSITIVE": "POSITIVO"}
+            sentiment_label = label_map.get(sentiment_label, sentiment_label)
+
+            # ➤ Clasificación con DistilBERT fine-tuned propio
+            custom_sentiment = custom_analyzer(question)[0]
+            custom_label = custom_sentiment['label']
+            custom_score = round(custom_sentiment['score'], 3)
+            custom_label = label_map.get(custom_label, custom_label)
+
+            # 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']}  
+            ---""")