Add Gemini LLM cal request

.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,16 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="2">
+            <item index="0" class="java.lang.String" itemvalue="torch" />
+            <item index="1" class="java.lang.String" itemvalue="bs4" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11 (seminarLabAI)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (seminarLabAI)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/seminarLabAI.iml" filepath="$PROJECT_DIR$/.idea/seminarLabAI.iml" />
+    </modules>
+  </component>
+</project>

.idea/seminarLabAI.iml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.11 (seminarLabAI)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

README.md

@@ -11,3 +11,6 @@ license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+## How to set HuggingFace's Space applications
+  - [How to Use Git with Hugging Face: From Cloning to Pushing with Access Token Verification ](https://dev.to/sh20raj/how-to-use-git-with-hugging-face-from-cloning-to-pushing-with-access-token-verification-5711)

app.py

@@ -1,7 +1,181 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+HISTORY_ANSWER = ''
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+with gr.Blocks() as demo:
+    gr.Markdown("SermonLab AI Demo.")
+    with gr.Tab("Preparando mi Sermón"):
+        text_input = gr.Textbox(label = "Tópico del sermón")
+
+        with gr.Accordion("Contemplando y Proclamando", open=False):
+          checkButton =  gr.Checkbox(
+               value = False,
+               label = "Mantener historial"
+          )
+          with gr.Row():
+             with gr.Tab("Contemplando"):
+              inbtwContemplando = gr.Button(f"Devocionalmente: {contemplandoQuestion['DEVOCIONALMENTE']}")
+              inbtwContemplandoOne = gr.Button(f"Exégesis: {contemplandoQuestion['EXÉGESIS']}")
+              inbtwContemplandoTwo = gr.Button(f"Cristo: {contemplandoQuestion['CRISTO']}")
+              inbtwContemplandoTree = gr.Button(f"Arco Redentor: {contemplandoQuestion['ARCO REDENTOR']}")
+              inbtwContemplandoFour = gr.Button(f"Evangelión: {contemplandoQuestion['EVANGELION']}")
+              inbtwContemplandoFourOne = gr.Button(f"Evangelión: {contemplandoQuestion['EVANGELION_TWO']}")
+
+             with gr.Tab("Proclamando"):
+              inbtwProclamando = gr.Button(f"Público: {proclamandoQuestion['PÚBLICO']}")
+              inbtwProclamandoOne = gr.Button(f"Historia: {proclamandoQuestion['HISTORIA']}")
+              inbtwProclamandoTwo = gr.Button(f"Expectativas: {proclamandoQuestion['EXPECTATIVAS']}")
+              inbtwProclamandoTwoTwo = gr.Button(f"Expectativas: {proclamandoQuestion['EXPECTATIVAS_TWO']}")
+
+
+        text_output = gr.Textbox(label = "Respuesta", lines = 10)
+
+        text_button = gr.Button("Crear")
+
+        text_download = gr.DownloadButton(
+            label = "Descargar",
+            value = fileAddresToDownload,
+            every = 10
+            )
+
+        inbtwContemplando.click(
+                  fn = lambda x: predictContemplando(f"DEVOCIONALMENTE"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        inbtwContemplandoOne.click(
+                  fn = lambda x: predictContemplando(f"EXÉGESIS"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        inbtwContemplandoTwo.click(
+                  fn = lambda x: predictContemplando(f"CRISTO"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        inbtwContemplandoTree.click(
+                  fn = lambda x: predictContemplando(f"ARCO REDENTOR"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        inbtwContemplandoFour.click(
+                  fn = lambda x: predictContemplando(f"EVANGELION"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        inbtwContemplandoFourOne.click(
+                  fn = lambda x: predictContemplando(f"EVANGELION_TWO"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        ##---------------------------------------------------------------------
+
+        inbtwProclamando.click(
+                  fn = lambda x: predictProclamando(f"PÚBLICO"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        inbtwProclamandoOne.click(
+                  fn = lambda x: predictProclamando(f"HISTORIA"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        inbtwProclamandoTwo.click(
+                  fn = lambda x: predictProclamando(f"EXPECTATIVAS"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        inbtwProclamandoTwoTwo.click(
+                  fn = lambda x: predictProclamando(f"EXPECTATIVAS_TWO"),
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+
+        text_button.click(
+                  fn = predictFromInit,
+                  inputs = text_input,
+                  outputs = text_output
+          )
+
+        text_download.click(
+                  fn = downloadSermonFile,
+                  inputs = text_output
+          )
+    with gr.Tab("Obtener guía de la comunidad (Preguntas)"):
+        with gr.Row():
+            #Bibliografy about components
+            # File (https://www.gradio.app/docs/gradio/file)
+            # Download Button (https://www.gradio.app/docs/gradio/downloadbutton)
+            with gr.Column():
+              file_input_question = gr.File( )
+              upload_button_question = gr.UploadButton("Click to Upload a File", file_types = ['.pdf'], file_count = "multiple")
+            with gr.Column():
+                temp_slider_question = gr.Slider(
+                  minimum=1,
+                  maximum=10,
+                  value=1,
+                  step=1,
+                  interactive=True,
+                  label="Preguntas",
+                )
+                text_output_question = gr.Textbox(label = "Respuesta", lines = 10)
+        text_button_question = gr.Button("Crear guía de preguntas")
+        text_download_question = gr.DownloadButton(
+            label = "Descargar",
+            value = fileAddresToDownload,
+            every = 10
+            )
+
+        text_button_question.click(
+              fn = predictQuestionBuild,
+              outputs = text_output_question
+        )
+
+        upload_button_question.upload(upload_file_ex, inputs= upload_button_question, outputs = [file_input_question, text_output_question])
+
+
+    with gr.Tab("Obtener guía de la comunidad (Devocionario)"):
+        with gr.Row():
+            #Bibliografy about components
+            # File (https://www.gradio.app/docs/gradio/file)
+            # Download Button (https://www.gradio.app/docs/gradio/downloadbutton)
+
+            with gr.Column():
+              file_input_devotions = gr.File( )
+              upload_button_devotion = gr.UploadButton("Click to Upload a File", file_types = ['.pdf'], file_count = "multiple")
+
+            with gr.Column():
+                temp_slider_question = gr.Slider(
+                  minimum=1,
+                  maximum=10,
+                  value=1,
+                  step=1,
+                  interactive=True,
+                  label="Cantidad",
+                )
+                text_output_devotions = gr.Textbox(label = "Respuesta", lines = 10)
+        text_button_devotion = gr.Button("Crear")
+        text_download_question = gr.DownloadButton(
+            label = "Descargar",
+            value = fileAddresToDownload,
+            every = 10
+            )
+
+        text_button_devotion.click(
+              fn = predictDevotionBuild,
+              outputs = text_output_devotions
+          )
+
+        upload_button_devotion.upload(upload_file_ex, inputs= upload_button_devotion, outputs = [file_input_devotions, text_output_devotions])
+
+demo.launch(  )

llm_call.py

@@ -0,0 +1,43 @@
+import os
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from google.colab import userdata
+
+class GeminiLLM():
+    def __init__(self):
+        self.ACCESS_TOKEN = os.getenv('GOOGLE_GEMINI_TOKEN')
+        self.model_name = "gemini-pro"
+
+    def getEmbeddingsModel(self):
+        self.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        return self.embeddings
+
+    def getRetriver(self, documents ):
+        vectorstore = Chroma.from_documents(
+            documents = documents,
+            embedding = self.embeddings,
+            persist_directory = "chroma_db_dir",  # Local mode with in-memory storage only
+            collection_name="sermon_lab_ai"
+        )
+
+        retriever = vectorstore.as_retriever(
+            search_kwargs={"k": 3}
+        )
+
+        return (retriever, vectorstore)
+
+    def getLLM(self, documents ):
+        if os.getenv('GOOGLE_GEMINI_TOKEN') is None:
+            raise ValueError("GOOGLE_GEMINI_TOKEN environment variable not set")
+        else:
+            self.llm  = ChatGoogleGenerativeAI(
+                model = self.model_name,
+                temperature = 0.7,
+                top_k = 40,
+                top_p = 1
+            )
+
+            return self.llm

packages.txt

@@ -0,0 +1 @@
+wkhtmltopdf

requirements.txt

@@ -0,0 +1,15 @@
+sentence-transformers
+transformers
+einops
+accelerate
+langchain_community
+langchain
+pypdf
+tiktoken
+unstructured
+chromadb
+google-generativeai
+langchain-google-genai
+pdfkit
+gradio
+pypdf

seminar_edition_ai.py

@@ -0,0 +1,280 @@
+from langchain.docstore.document import Document
+from langchain.chains.question_answering import load_qa_chain
+import chromadb
+from datetime import datetime
+import os
+from datetime import datetime
+import pdfkit
+from langchain.chains.question_answering import load_qa_chain
+
+bookQuestion = dict()
+
+contemplandoQuestion = {
+    'DEVOCIONALMENTE':'¿Cómo estimula Dios su corazón a través de Su Palabra?',
+    'EXÉGESIS':'Cuál es el contexto de este pasaje?',
+    'CRISTO':'¿Cómo se comprende este texto a la luz de Cristo?',
+    'ARCO REDENTOR':'¿Cómo encaja este texto en la metanarrativa de las Escrituras?',
+    'EVANGELION': '¿Cómo se declara el evangelio en este texto?',
+    'EVANGELION_TWO': '¿Cómo interpretamos este texto a la luz del evangelio?',
+     }
+
+proclamandoQuestion = {
+     'PÚBLICO':'¿Cuáles son los ídolos en los corazones de las personas que rechazarían el evangelio de Cristo?',
+     'HISTORIA':'¿Cómo el guión de su predicación comunica la historia de Dios?',
+     'EXPECTATIVAS': '¿Qué espera Dios que hagan como respuesta a esta predicación?',
+     'EXPECTATIVAS_TWO': '¿Cuáles son sus expectativas divinas como predicador de este mensaje?',
+ }
+
+
+bookQuestion['Contemplando'] = contemplandoQuestion
+bookQuestion['Proclamando'] = proclamandoQuestion
+
+HISTORY_ANSWER = ""
+
+DIRECTORY_PATH_TO_DOWNLOAD = 'data/sermon_lab_ai/download_files'
+
+if not os.path.exists(DIRECTORY_PATH_TO_DOWNLOAD):
+  os.makedirs(f"{DIRECTORY_PATH_TO_DOWNLOAD}")
+
+def getCurrentFileName():
+  now = datetime.now()
+  strNow = now.strftime("%m%d%Y_%H%M%S")
+  return f"sermonDay_{strNow}.pdf"
+
+fileAddresToDownload = f"{DIRECTORY_PATH_TO_DOWNLOAD}{os.sep}{getCurrentFileName()}"
+FILE_PATH_NAME = fileAddresToDownload
+
+def updatePromptTemplate(promptTemplate, inputVariablesTemplate):
+    prompt = PromptTemplate(template = promptTemplate,
+                        input_variables = inputVariablesTemplate)
+    chain = load_qa_chain(llm, chain_type="stuff", prompt = prompt)
+    return chain
+def predict(query):
+  chain = updatePromptTemplate(
+      sermonPromptMenuGemini['BUILD_PREPARE_QUESTIONS'],
+       ['question','SERMON_CONTEXT','context']
+    )
+
+  if query != '':
+    global retriever
+    answer = askQuestion(
+        query,
+        chain,
+        retriever,
+        topic = query,
+        KEY = 'question'
+    )
+    answer = (answer.split("<|assistant|>")[-1]).strip()
+    HISTORY_ANSWER = answer
+
+    return answer
+  else:
+    return query
+
+def predictContemplando(queryKey):
+  #Call to LLM LangChaing inference
+  query = contemplandoQuestion[queryKey]
+  return predict(query)
+
+
+def predictProclamando(queryKey):
+  #Call to LLM LangChaing inference
+  query = proclamandoQuestion[queryKey]
+  return predict(query)
+
+####
+#
+####
+def predictFromInit(sermonTopic):
+  global HISTORY_ANSWER
+  keyStr = 'SERMON_TOPIC'
+
+  if HISTORY_ANSWER == '':
+    chain = updatePromptTemplate(
+        sermonPromptMenuGemini['BUILD_INIT'],
+        [keyStr,'CANT_VERSICULOS','context']
+        )
+  else:
+    chain = updatePromptTemplate(
+        sermonPromptMenuGemini['BUILD_EMPTY'],
+        ['BIBLE_VERSICLE','context']
+        )
+    keyStr = 'BIBLE_VERSICLE'
+
+  global retriever
+  answer = askQuestionInit(
+      '',
+      chain,
+      retriever,
+      topic = sermonTopic,
+      KEY = keyStr
+    )
+
+  #Create a new document and build a retriver
+  if answer != '':
+    doc =  Document(page_content="text", metadata = {"source": "local"})
+
+    vectorstore = Chroma.from_documents(
+      documents = [doc],
+      embedding = embed_model,
+      persist_directory="chroma_db_dir_sermon",  # Local mode with in-memory storage only
+      collection_name="sermon_lab_ai"
+    )
+
+    retriever = vectorstore.as_retriever(
+        search_kwargs = {"k": 3}
+    )
+
+
+  HISTORY_ANSWER = answer
+
+  return answer
+
+####
+#
+####
+def predictQuestionBuild(sermonTopic):
+  chain = updatePromptTemplate(
+      sermonPromptMenuGemini['BUILD_QUESTION'],
+       ['SERMON_IDEA', 'context']
+      )
+  global retriever
+  answer = askQuestionEx(
+      '',
+      chain,
+      retriever,
+      topic = sermonTopic,
+      KEY = 'SERMON_IDEA'
+    )
+
+  return answer
+
+####
+#
+####
+def predictDevotionBuild(sermonTopic):
+  chain = updatePromptTemplate(
+      sermonPromptMenuGemini['BUILD_REFLECTIONS'],
+       ['SERMON_IDEA', 'context']
+      )
+  global retriever
+  global HISTORY_ANSWER
+  answer = askQuestionEx(
+      HISTORY_ANSWER,
+      chain,
+      retriever,
+      topic = sermonTopic,
+      KEY = 'SERMON_IDEA'
+    )
+
+  return answer
+
+
+# A utility function for answer generation
+def askQuestion(
+    question,
+    _chain,
+    _retriever,
+    topic = 'el amor de Dios',
+    KEY = 'SERMON_TOPIC'
+    ):
+
+   #Obtener los Chunks relevantes a la pregunta en el RAG
+   #print(f" Question: {question}")
+
+   context = _retriever.get_relevant_documents(question)
+
+   #print("----  Contexto ----")
+   #print(context)
+   #print("____________________GLOBAL________")
+
+   global HISTORY_ANSWER
+
+   #print (HISTORY_ANSWER)
+
+   return (
+         _chain({
+             KEY: topic,
+             'SERMON_CONTEXT': HISTORY_ANSWER,
+             "input_documents": context,
+             "question": question
+             },
+            return_only_outputs=True)
+         )['output_text']
+
+
+ A utility function for answer generation
+def askQuestionEx(
+    question,
+    _chain,
+    _retriever,
+    topic = 'el amor de Dios',
+    KEY = 'SERMON_TOPIC'
+    ):
+
+   #Obtener los Chunks relevantes a la pregunta en el RAG
+   #print(f" Question: {question}")
+
+   context = _retriever.get_relevant_documents(question)
+
+   #print("----  Contexto ----")
+   #print(context)
+   #print("____________________GLOBAL________")
+
+   global HISTORY_ANSWER
+   #print (HISTORY_ANSWER)
+
+   return (
+         _chain({
+             KEY: topic,
+             "input_documents": context,
+             "question": question
+             },
+            return_only_outputs=True)
+         )['output_text']
+
+# A utility function for answer generation
+def askQuestionInit(
+    question,
+    _chain,
+    _retriever,
+    topic = 'el amor de Dios',
+    KEY = 'SERMON_TOPIC'
+    ):
+
+   #Obtener los Chunks relevantes a la pregunta en el RAG
+   context = _retriever.get_relevant_documents(question)
+
+   settings = {
+             KEY: topic,
+             "input_documents": context,
+             "question": question
+             }
+
+   if KEY == 'SERMON_TOPIC':
+     settings['CANT_VERSICULOS'] = 5
+
+   return (
+         _chain(
+            settings,
+            return_only_outputs=True)
+         )['output_text']
+
+
+
+
+
+
+
+
+def downloadSermonFile(answer):
+
+  if os.path.exists(FILE_PATH_NAME):
+    os.remove(FILE_PATH_NAME)
+
+  pdfkit.from_string(
+        answer,
+       FILE_PATH_NAME
+    )
+
+  return ""