LLM (LANGCHAIN GEN AI) folder

Artificial Intelligence/Advanced/LLM (LANGCHAIN GEN AI)/LLM3-Gemini Pro LLM Application/LLM3-Gemini Pro LLM Application.py

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+##LLM5-Gemini Pro LLM Application
+
+
+from dotenv import load_dotenv
+load_dotenv()
+
+import streamlit as st
+import os
+import google.generativeai as genai
+
+genai.configure(api_key = os.getenv("GOOGLE_API_KEY"))
+
+## function to load Gemini Pro Model and get response
+model=genai.GenerativeModel("gemini-pro")
+chat= model.start_chat(history=[])
+
+def get_gemini_response(question):
+    response= chat.send_message(question,stream=True)
+    return response
+
+##initialize our streamlit app
+
+st.set_page_config(page_title="Q&A Demo")
+st.header("Gemini LLM Application")
+
+## initialize session state for chat history if it doesn't exist
+if 'chat_history' not in st.session_state:
+    st.session_state['chat_history']=[]
+
+
+input=st.text_input("Input:",key="input")
+submit=st.button("Ask the question")
+
+if submit and input:
+    response= get_gemini_response(input)
+    ## Add user query and response to session chat hostory
+    st.session_state['chat_history'].append(("You",input))
+    st.subheader("The Response is")
+    for chunk in response:
+        st.write(chunk.text)
+        st.session_state['chat_history'].append(("Bot",chunk.text))
+st.subheader("The chat history is ")
+
+for role,text in st.session_state['chat_history']:
+    st.write(f"{role}:{text}")
+
+
+
+
+
+

Artificial Intelligence/Advanced/LLM (LANGCHAIN GEN AI)/LLM3-Gemini Pro LLM Application/requirements.txt

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+streamlit
+google-generativeai
+python-dotenv

Artificial Intelligence/Advanced/LLM (LANGCHAIN GEN AI)/LLM4-Invoice Extractor using Gemini Pro Vision/LLM4-Invoice Extractor using Gemini Pro Vision.py

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+##LLM6-Invoice Extractor using Gemini Pro Vision
+
+
+from dotenv import load_dotenv
+load_dotenv() ##load all the environment variables from .env  
+
+import streamlit as st
+import os
+from PIL import Image
+import google.generativeai as genai
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+### Function to load Gemini Pro Vision
+model= genai.GenerativeModel('gemini-pro-vision')
+def get_gemini_response(input,image,prompt):
+    response = model.generate_content([input,image[0],prompt])
+    return response.text  
+
+
+def input_image_details(uploaded_file):
+    if uploaded_file is not None:
+        #Read the file into bytes
+        bytes_data= uploaded_file.getvalue()
+
+        image_parts=[
+            {
+                "mime_type": uploaded_file.type, #Get the mmime type of the uploaded file
+                "data":bytes_data
+            }
+        ]
+        return image_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+
+
+###initialize our streamlit app
+
+st.set_page_config(page_title="MultiLanguage Invoice Extractor")
+
+st.header("MultiLanguage Invoice Extractor")
+input=st.text_input("Input Prompt: ",key="input")
+uploaded_file=st.file_uploader("Choose an image of the invoice....",type=["jpg","jpeg","png"])
+image=""
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image,caption="Uploaded Image",use_column_width=True)
+
+submit=st.button("Tell me about the invoice")    
+input_prompt="""
+You are an expert in understanding invoices. We will upload an image as invoice and you will have to answer any question based on the uploaded invoice image
+"""                                                                   
+
+## If submit button is clicked
+if submit:
+    image_data = input_image_details(uploaded_file)
+    response= get_gemini_response(input_prompt,image_data,input)
+    st.subheader("The Response is")
+    st.write(response)
+
+

Artificial Intelligence/Advanced/LLM (LANGCHAIN GEN AI)/LLM4-Invoice Extractor using Gemini Pro Vision/requirements.txt

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+streamlit
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+chromadb 

Artificial Intelligence/Advanced/LLM (LANGCHAIN GEN AI)/README.md

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+                                    LLM (LANGCHAIN GEN AI)
+
+This pack contains a series of projects developed as part of the LangChain initiative, demonstrating various applications and use cases leveraging language models and generative AI. Each project is designed to showcase the capabilities of different technologies and platforms.                        
+
+Requirements:
+Python 3.8 or higher(python 3.10 for LLM5 and LLM6 files)
+Streamlit
+langchain library
+OpenAI API key
+Hugging Face API token
+LLaMA 2 model file
+Google's GenerativeAI API Key
+Pillow (Python Imaging Library)
+
+
+Projects Overview:
+
+1. LLM1-Basics of LLM
+
+    1.1. LLM1-Basics of LLM 
+This project introduces the LangChain library and demonstrates how to use it to interact with OpenAI and Hugging Face models. The examples cover querying information, chaining models for sequential tasks, and using prompt templates.
+
+      Pre-requisites:
+      Set your OpenAI API key and Hugging Face API token as environment variables:
+      OPEN_API_KEY='your_openai_api_key'
+      HUGGINGFACEHUB_API_TOKEN='your_huggingface_api_token'
+
+   1.2. LangChaiChatmodels with ChatOpenAI
+Project LangChain 02 showcases the integration of ChatOpenAI for interactive conversations. It involves setting up ChatOpenAI, providing system and human messages, and obtaining AI-generated responses.
+
+      Pre-requisites: 
+      Set up your OpenAI API key as an environment variable:
+      OPEN_API_KEY='your-api-key-here'     
+
+2. LLM2-Querying PDF with AstraDB
+This project demonstrates a question-answering demo using Astra DB and LangChain, powered by Vector Search. It involves setting up a Serverless Cassandra with Vector Search on Astra DB and querying PDF documents.
+
+      Pre-requisites: 
+      You need a Serverless Cassandra with Vector Search database on ASTRA DB to run this demo.You should get a DB Token with role Database Administrator 
+      and copy your Database ID.You also need an OpenAI API Key.   
+
+3. LLM5-Gemini Pro LLM Application
+This Streamlit application serves as a Q&A interface, integrating with Google's GenerativeAI Gemini Pro model. It configures the environment, establishes a Streamlit interface, and facilitates communication with the Gemini Pro model through user inputs. Users can input questions, and the app displays responses from the AI model. It maintains a session-based chat history, showing an ongoing conversation between the user and the AI.
+
+      Pre-requisites:     
+      Set up your environment variables:
+      Create a .env file in the project root.
+      Add your Google GenerativeAI API key:
+      GOOGLE_API_KEY=your_google_generativeai_api_key
+
+
+4. LLM6-Invoice Extractor using Gemini Pro Vision
+A Streamlit web application titled "MultiLanguage Invoice Extractor" leveraging Google's Generative AI Gemini Pro Vision model to analyze and extract data from uploaded invoice images. Users can upload invoice images, input prompts, and get AI-generated insights.
+
+      Pre-requisites: 
+      Set up your environment variables:
+      Create a .env file in the project root.
+      Add your Google GenerativeAI API key:
+      GOOGLE_API_KEY=your_google_generativeai_api_key 
+
+
+Note:
+To run the LLM5 and LLM6 file (i.e. '---.py' python files),follow the following steps:
+1. Create a conda environment with python version 3.10.
+   conda create --name myvenv python=3.10 
+2. Activate the conda environment.
+   conda activate myvenv
+3. Install all the dependencies.
+   pip install -r requirements.txt
+4. Run the file.
+   streamlit run 'file_name.py' 
+6. Copy and paste the displayed url in other tab outside the hub.            
+
+
+