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Update app.py
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app.py
CHANGED
@@ -4,6 +4,9 @@ import os
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import google.generativeai as genai
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from puv_formulas import puv_formulas
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from styles import apply_styles
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# Cargar variables de entorno
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load_dotenv()
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@@ -12,7 +15,7 @@ load_dotenv()
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genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
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# Función para obtener la respuesta del modelo Gemini
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def get_gemini_response(product_service, target_audience, skills, formula_type, temperature):
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# Check if at least target audience is provided
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if not target_audience:
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return "El campo de público objetivo es obligatorio."
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@@ -32,6 +35,11 @@ def get_gemini_response(product_service, target_audience, skills, formula_type,
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if skills:
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business_info += f"My Skills/Expertise: {skills}\n"
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model = genai.GenerativeModel('gemini-2.0-flash')
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full_prompt = f"""
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You are a UVP (Unique Value Proposition) expert. Analyze (internally only, do not output the analysis) the following information:
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@@ -39,6 +47,7 @@ def get_gemini_response(product_service, target_audience, skills, formula_type,
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{business_info}
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Formula Type: {formula_type}
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{formula["description"]}
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EXAMPLE TO FOLLOW:
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{formula["examples"]}
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@@ -81,7 +90,12 @@ def get_gemini_response(product_service, target_audience, skills, formula_type,
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3. [Third UVP]
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"""
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return response.parts[0].text if response and response.parts else "Error generating content."
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# Configurar la aplicación Streamlit
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@@ -119,6 +133,58 @@ with col1:
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placeholder="Ejemplo: Experiencia en marketing digital, certificación en SEO..."
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)
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with st.expander("Opciones avanzadas"):
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formula_type = st.selectbox(
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"Fórmula PUV:",
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@@ -142,7 +208,9 @@ with col2:
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target_audience,
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skills,
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formula_type,
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temperature
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)
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st.write("### Propuestas Únicas de Valor")
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st.write(response)
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import google.generativeai as genai
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from puv_formulas import puv_formulas
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from styles import apply_styles
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import PyPDF2
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import docx
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from PIL import Image
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# Cargar variables de entorno
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load_dotenv()
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genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
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# Función para obtener la respuesta del modelo Gemini
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def get_gemini_response(product_service, target_audience, skills, formula_type, temperature, file_content="", image_parts=None):
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# Check if at least target audience is provided
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if not target_audience:
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return "El campo de público objetivo es obligatorio."
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if skills:
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business_info += f"My Skills/Expertise: {skills}\n"
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# Add file content if available
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reference_info = ""
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if file_content:
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reference_info = f"\nREFERENCE MATERIAL:\n{file_content}\n"
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model = genai.GenerativeModel('gemini-2.0-flash')
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full_prompt = f"""
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You are a UVP (Unique Value Proposition) expert. Analyze (internally only, do not output the analysis) the following information:
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{business_info}
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Formula Type: {formula_type}
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{formula["description"]}
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{reference_info}
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EXAMPLE TO FOLLOW:
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{formula["examples"]}
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3. [Third UVP]
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"""
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# Handle text-only or text+image requests
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if image_parts:
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response = model.generate_content([full_prompt, image_parts], generation_config={"temperature": temperature})
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else:
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response = model.generate_content([full_prompt], generation_config={"temperature": temperature})
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return response.parts[0].text if response and response.parts else "Error generating content."
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# Configurar la aplicación Streamlit
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placeholder="Ejemplo: Experiencia en marketing digital, certificación en SEO..."
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)
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# Añadir cargador de archivos
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uploaded_file = st.file_uploader("📄 Archivo o imagen de referencia",
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type=['txt', 'pdf', 'docx', 'jpg', 'jpeg', 'png'])
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file_content = ""
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is_image = False
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image_parts = None
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if uploaded_file is not None:
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file_type = uploaded_file.name.split('.')[-1].lower()
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# Manejar archivos de texto
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if file_type in ['txt', 'pdf', 'docx']:
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if file_type == 'txt':
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try:
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file_content = uploaded_file.read().decode('utf-8')
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except Exception as e:
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st.error(f"Error al leer el archivo TXT: {str(e)}")
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file_content = ""
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elif file_type == 'pdf':
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try:
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pdf_reader = PyPDF2.PdfReader(uploaded_file)
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file_content = ""
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for page in pdf_reader.pages:
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file_content += page.extract_text() + "\n"
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except Exception as e:
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st.error(f"Error al leer el archivo PDF: {str(e)}")
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file_content = ""
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elif file_type == 'docx':
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try:
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doc = docx.Document(uploaded_file)
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file_content = "\n".join([para.text for para in doc.paragraphs])
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except Exception as e:
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st.error(f"Error al leer el archivo DOCX: {str(e)}")
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file_content = ""
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# Manejar archivos de imagen
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elif file_type in ['jpg', 'jpeg', 'png']:
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try:
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image = Image.open(uploaded_file)
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image_bytes = uploaded_file.getvalue()
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image_parts = {
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"mime_type": uploaded_file.type,
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"data": image_bytes
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}
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is_image = True
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except Exception as e:
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st.error(f"Error al procesar la imagen: {str(e)}")
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is_image = False
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with st.expander("Opciones avanzadas"):
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formula_type = st.selectbox(
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"Fórmula PUV:",
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target_audience,
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skills,
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formula_type,
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temperature,
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file_content,
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image_parts
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)
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st.write("### Propuestas Únicas de Valor")
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st.write(response)
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