Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import textwrap
+import google.generativeai as genai
+import os
+import PyPDF2
+
+# Function to display formatted Markdown text
+def to_markdown(text):
+    text = text.replace('•', '  *')
+    return textwrap.indent(text, '> ', predicate=lambda _: True)
+
+# Function to generate content using Gemini API
+def generate_gemini_content(prompt, model_name='gemini-pro-vision', image=None):
+    model = genai.GenerativeModel(model_name)
+    if not image:
+        st.warning("Por favor, agrega una imagen para usar el modelo gemini-pro-vision.")
+        return None
+
+    response = model.generate_content([prompt, image])
+    return response
+
+# Function to extract text from PDFs in a folder
+def extract_text_from_pdf(pdf_path):
+    text = ""
+    with open(pdf_path, 'rb') as file:
+        pdf_reader = PyPDF2.PdfFileReader(file)
+        num_pages = pdf_reader.numPages
+        for page_num in range(num_pages):
+            page = pdf_reader.getPage(page_num)
+            text += page.extractText()
+    return text
+
+# Function to process PDFs in a folder
+def process_pdfs_in_folder(folder_path):
+    pdf_texts = {}
+    for filename in os.listdir(folder_path):
+        if filename.endswith(".pdf"):
+            pdf_path = os.path.join(folder_path, filename)
+            text = extract_text_from_pdf(pdf_path)
+            pdf_texts[filename] = text
+    return pdf_texts
+
+# Streamlit app
+def main():
+    st.set_page_config(page_title="MAX Chatbot - INIF", page_icon="🤖")
+
+    # Configurar la API key de Gemini (reemplazar con tu clave de API de Gemini)
+    genai.configure(api_key='AIzaSyA4k6JoFNZsf8L1ixLMMRjEMoBPns5SHZk')
+
+    st.title("MAX Chatbot - INIF")
+    st.sidebar.title("Configuración de MAX Chatbot")
+
+    # Configurar la API key de INIF
+    inif_api_key = 'AIzaSyA4k6JoFNZsf8L1ixLMMRjEMoBPns5SHZk'
+    genai.configure(api_key=inif_api_key)
+
+    # Seleccionar el modelo Gemini
+    select_model = st.sidebar.selectbox("Selecciona el modelo", ["gemini-pro", "gemini-pro-vision"])
+
+    # Inicializar la sesión de chat
+    chat = genai.GenerativeModel(select_model).start_chat(history=[])
+
+    # Definir función para obtener respuesta del modelo Gemini
+    def get_response(messages):
+        response = chat.send_message(messages, stream=True)
+        return response
+
+    # Historial del chat
+    if "messages" not in st.session_state:
+        st.session_state["messages"] = []
+
+    messages = st.session_state["messages"]
+
+    # Obtener texto de los PDFs en la carpeta "PDF" en la misma ruta de ejecución
+    pdf_folder_path = "PDF"
+    pdf_texts = process_pdfs_in_folder(pdf_folder_path)
+
+    # Incorporar el contenido de los PDFs al contexto del INIF
+    inif_context = (
+        "I am an informative data analyst chatbot named MAX, working for the National Institute of Fraud Research and Prevention (INIF), dedicated to fraud prevention and mitigation."
+        " If you have questions related to fraud or prevention, feel free to ask. For inquiries about other topics, I'll redirect you to the fraud prevention context."
+        "\n\nContact Information for INIF:"
+        "\nPhone: +57 317 638 94 71"
+        "\nEmail: atencionalcliente@inif.com.co"
+        "\n\nOur Mission:"
+        "\nTo be the most reliable engine of knowledge, research, and information in Colombia, capable of preventing and combating fraud through synergy between our team and companies."
+        "\n\nOur Vision:"
+        "\nTo lead the construction of a more honest culture, allowing us to progress as a society."
+    )
+
+    for pdf_name, pdf_text in pdf_texts.items():
+        inif_context += f"\n\n**{pdf_name}**:\n{pdf_text}"
+
+    # Mostrar mensajes del historial
+    if messages:
+        for message in messages:
+            role, parts = message.values()
+            if role.lower() == "user":
+                st.markdown(f"Tú: {parts[0]}")
+            elif role.lower() == "model":
+                st.markdown(f"Assistant: {to_markdown(parts[0])}")
+
+    # Entrada del usuario
+    user_input = st.text_area("Tú:")
+
+    # Concatenar el contexto del INIF al input del usuario
+    user_input_with_context = f"{user_input}\n\n{inif_context}"
+
+    # Get optional image input if the model selected is 'gemini-pro-vision'
+    image_file = None
+    if select_model == 'gemini-pro-vision':
+        image_file = st.file_uploader("Sube una imagen (si aplica):", type=["jpg", "jpeg", "png"])
+
+        # Display image if provided
+        if image_file:
+            st.image(image_file, caption="Imagen subida", use_column_width=True)
+
+    # Botón para enviar mensaje o generar contenido según el modelo seleccionado
+    if st.button("Enviar / Generar Contenido"):
+        if user_input:
+            messages.append({"role": "user", "parts": [user_input]})
+            if select_model == 'gemini-pro-vision':
+                # Modelo Gemini Vision Pro seleccionado
+                if not image_file:
+                    st.warning("Por favor, proporciona una imagen para el modelo gemini-pro-vision.")
+                else:
+                    image = Image.open(image_file)
+                    response = generate_gemini_content(user_input_with_context, model_name=select_model, image=image)
+                    if response:
+                        if response.candidates:
+                            parts = response.candidates[0].content.parts
+                            generated_text = parts[0].text if parts else "No se generó contenido."
+                            st.markdown(f"Assistant: {to_markdown(generated_text)}")
+                            messages.append({"role": "model", "parts": [generated_text]})
+                        else:
+                            st.warning("No se encontraron candidatos en la respuesta.")
+            else:
+                # Otros modelos Gemini seleccionados
+                response = get_response(user_input_with_context)
+
+                # Mostrar respuesta del modelo solo una vez
+                res_text = ""
+                for chunk in response:
+                    res_text += chunk.text
+                st.markdown(f"Assistant: {to_markdown(res_text)}")
+                messages.append({"role": "model", "parts": [res_text]})
+
+    # Actualizar historial de mensajes en la sesión de Streamlit
+    st.session_state["messages"] = messages
+
+if __name__ == "__main__":
+    main()