Hugging Face's logo

Spaces:
acecalisto3
/
AIsmart
Sleeping

App Files Community
AIsmart
File size: 3,368 Bytes 
becbcc8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
 
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.tools import DuckDuckGoSearchRun

# Function to generate video script
def generate_script(prompt,duration,origin,budget,api_key):

    # Template for generating 'Itinerary' using search engine
    itinerary_template = PromptTemplate(
        input_variables = ['location','duration','DuckDuckGo_Search','expense','origin'], 
        template='Please come up with a detailed itinerary for a trip to {location} for a duration: {duration} days using this search data {DuckDuckGo_Search}. The maximum budget per person in INR is {expense}. The origin of the journey is {origin}. '
        )

    # Template for generating 'Conveyance Used' using search engine
    conveyance_template = PromptTemplate(
        input_variables = ['location', 'origin', 'DuckDuckGo_Search'], 
        #template='Based on this itinerary: {itinerary}, please mention the conveyance to be taken during the trip while going from one place to another using this search data {DuckDuckGo_Search} '
        template='Please mention the different ways to reach the {location} from {origin} and the different modes of travel using this search data {DuckDuckGo_Search} '
    )

    # Template for generating 'Cuisines' using search engine
    cuisines_template = PromptTemplate(
        input_variables = ['itinerary', 'DuckDuckGo_Search'], 
        template='Based on this itinerary: {itinerary}, please mention the cuisines that I can try during the trip using this search data {DuckDuckGo_Search} '
    )

    # Template for generating 'Accomodation' using search engine
    accomodation_template = PromptTemplate(
        input_variables = ['itinerary', 'DuckDuckGo_Search'], 
        template='Based on this itinerary: {itinerary}, please mention the accomodation/hotel that I can stay in during the trip using this search data {DuckDuckGo_Search} '
    )

    #Setting up OpenAI LLM
    llm = OpenAI(temperature=0.5, openai_api_key=api_key, model_name='gpt-3.5-turbo') 
    
    #Creating chain for 'Itinerary', 'Conveyance', 'Cuisines', 'Accomodation'
    itinerary_chain = LLMChain(llm=llm, prompt=itinerary_template, verbose=True)
    conveyance_chain = LLMChain(llm=llm, prompt=conveyance_template, verbose=True)
    cuisines_chain = LLMChain(llm=llm, prompt=cuisines_template, verbose=True)
    accomodation_chain = LLMChain(llm=llm, prompt=accomodation_template, verbose=True)

    
    # https://python.langchain.com/docs/modules/agents/tools/integrations/ddg
    search = DuckDuckGoSearchRun()
    search_result = search.run(prompt)

    # Executing the chains we created for 'Itinerary' by taking help of search engine 'DuckDuckGo'
    itinerary = itinerary_chain.run(location=prompt,duration=duration,DuckDuckGo_Search=search_result,expense=budget,origin=origin)

    # Executing the chains we created for 'Conveyance', 'Cuisines', 'Accomodation' by taking help of search engine 'DuckDuckGo'
    conveyance = conveyance_chain.run(location=prompt,origin=origin,DuckDuckGo_Search=search_result)
    hotel = accomodation_chain.run(itinerary=itinerary,DuckDuckGo_Search=search_result)
    cuisine = cuisines_chain.run(itinerary=itinerary,DuckDuckGo_Search=search_result)

    # Returning the output
    return search_result,itinerary,conveyance,hotel,cuisine