first

app.py

@@ -1,29 +1,26 @@
-# import openai
-from langchain_openai.llms import OpenAI
-from dotenv import load_dotenv
-import os
-
-from langchain_core.prompts import PromptTemplate
-from langchain.chains import LLMChain
-
+## Difference between Open AI and hugging face
 
-# os.environ["OPENAPI_KEY"]='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'
+from dotenv import load_dotenv
 load_dotenv()
-api_key = os.environ['OPEN_API_KEY'];
-llm = OpenAI(openai_api_key=api_key)
-
-mytemplate = """Question: {question}
-Answer: Let's think step by step."""
-
-# prompt = PromptTemplate.from_template(mytemplate)
+from langchain_openai import OpenAI
+llm = OpenAI()
+result= llm.invoke("What is capital of India")
+print(result)
 
-# mychain = LLMChain(llm=llm, prompt=prompt)
+## Use Huggingface to load google Model using langchain
 
-# question ="who is fastest man on earth?"
-# print(mychain.run(question))
+from langchain import HuggingFaceHub
+llm2 = HuggingFaceHub(
+    repo_id="google/flan-t5-large",
+    model_kwargs={"temperature":0, "max_length":180}
+)
 
-result= llm.generate(['two hit movies of bollywood ', 'two flop movies of bollywood'])
+results2= llm2("what is the capital of Sri lanka")
+print(results2)
 
-print(result.llm_output)
-print(result.generations[1][0].text)
+## Use Google GenAI chat models using langChain
 
+from langchain_google_genai import ChatGoogleGenerativeAI
+chat = ChatGoogleGenerativeAI(model="gemini-pro")
+results3 = chat.invoke("What is the capital of Pakistan")
+print(results3.content)

requirements.txt

@@ -0,0 +1,4 @@
+langchain
+openai
+streamlit
+langchain_google_genai