Update app.py

app.py

@@ -1,33 +1,102 @@
-import streamlit as st
+# import streamlit as st
+# from transformers import pipeline
+
+# # pipe = pipeline('sentiment-analysis')
+# text = st.text_area('enter text: ')
+
+# generator = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
+
+
+# technical_text = """
+# The CRISPR-Cas9 system enables precise genome editing by creating double-strand breaks at specific DNA locations, facilitating targeted genetic modifications.
+# """
+
+# # Prompt para transformación
+# prompt = f"Rewrite the following technical text in simple terms for a general audience:\n\n{text}\n\nSimplified version:"
+
+# # Generar texto transformado
+# result = generator(
+#     prompt,
+#     max_length=256,
+#     num_return_sequences=1,
+#     do_sample=True,
+#     temperature=0.1,
+#     top_p=0.9,
+#     repetition_penalty=1.1,
+# )
+
+# print(result[0]['generated_text'])
+
+
+# if text:
+#     out = pipe(text)
+#     st.json(out)
+
+
+
+
 from transformers import pipeline
+import json
+
+# Step 1: Rewriting the technical text in accessible language using T5 model
+simplifier = pipeline("summarization", model="t5-small")
+
+def simplify_text(text):
+    result = simplifier(text, max_length=100, min_length=50, do_sample=False)
+    return result[0]['summary_text']
+
+# Step 2: Translation to English, Arabic, and French using MarianMT models
+translator_en = pipeline("translation_es_to_en", model="Helsinki-NLP/opus-mt-es-en")
+translator_ar = pipeline("translation_es_to_ar", model="Helsinki-NLP/opus-mt-es-ar")
+translator_fr = pipeline("translation_es_to_fr", model="Helsinki-NLP/opus-mt-es-fr")
 
-# pipe = pipeline('sentiment-analysis')
-text = st.text_area('enter text: ')
+def translate_text(text):
+    translations = {
+        "english": translator_en(text)[0]['translation_text'],
+        "arabic": translator_ar(text)[0]['translation_text'],
+        "french": translator_fr(text)[0]['translation_text']
+    }
+    return translations
 
-generator = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
+# Step 3: Identify the main topic using DistilBERT
+classifier = pipeline("zero-shot-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+labels = ["Technology", "Science", "Health", "Business", "Education", "Other"]
 
+def identify_topic(text):
+    classification = classifier(text, candidate_labels=labels)
+    return classification['labels'][0]  # Main topic
 
-technical_text = """
-The CRISPR-Cas9 system enables precise genome editing by creating double-strand breaks at specific DNA locations, facilitating targeted genetic modifications.
-"""
+# Step 4: Detect the tone of the text using RoBERTa
+tone_analyzer = pipeline("sentiment-analysis", model="roberta-base")
 
-# Prompt para transformación
-prompt = f"Rewrite the following technical text in simple terms for a general audience:\n\n{text}\n\nSimplified version:"
+def detect_tone(text):
+    tone_result = tone_analyzer(text)[0]
+    return tone_result['label']  # This gives a general idea of the tone (positive, neutral, etc.)
 
-# Generar texto transformado
-result = generator(
-    prompt,
-    max_length=256,
-    num_return_sequences=1,
-    do_sample=True,
-    temperature=0.1,
-    top_p=0.9,
-    repetition_penalty=1.1,
-)
+# Step 5: Formatting results for web service
+def process_text_for_web_service(text):
+    simplified_text = simplify_text(text)
+    translations = translate_text(simplified_text)
+    main_topic = identify_topic(simplified_text)
+    tone = detect_tone(simplified_text)
+    
+    # Create a structured output
+    result = {
+        "original_text": text,
+        "simplified_text": simplified_text,
+        "translations": translations,
+        "main_topic": main_topic,
+        "tone": tone
+    }
+    
+    # Convert to JSON for web service
+    return json.dumps(result, ensure_ascii=False, indent=4)
 
-print(result[0]['generated_text'])
+# Example input text (in Spanish)
+input_text = "La inteligencia artificial (IA) está revolucionando la industria de la tecnología al permitir nuevas aplicaciones en múltiples campos, desde la salud hasta la educación."
 
+# Run the process
+formatted_output = process_text_for_web_service(input_text)
 
-if text:
-    out = pipe(text)
-    st.json(out)
+# Output the JSON formatted result
+print(formatted_output)