app.py

import os
import gradio as gr
from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
import openai
import requests

OPENAI_API_KEY = os.environ['OPENAI_API_KEY']
GOOGLE_MAPS_API = os.environ['GOOGLE_MAPS_API']


def get_location_data(search_term, location):
    # First, we get the latitude and longitude coordinates of the location
    url = "https://maps.googleapis.com/maps/api/geocode/json"
    params = {
        "address": location,
        "key": GOOGLE_MAPS_API
    }
    response = requests.get(url, params=params)
    location_data = response.json()["results"][0]["geometry"]["location"]
    
    # Next, we use the Places API nearbysearch endpoint to find places matching the search term
    url = "https://maps.googleapis.com/maps/api/place/nearbysearch/json"
    params = {
        "location": f"{location_data['lat']},{location_data['lng']}",
        "radius": "10000", # 5km radius
        #"type": search_term,
        "keyword" : search_term,
        "key": GOOGLE_MAPS_API
    }
    response = requests.get(url, params=params)
    results = response.json()["results"]
    
    # We only want the first 5 results
    results = results[:5]
    
    # For each result, we get the place details to retrieve the description and top reviews
    locations = []
    for result in results:
        place_id = result["place_id"]
        url = "https://maps.googleapis.com/maps/api/place/details/json"
        params = {
            "place_id": place_id,
            "fields": "name,formatted_address,formatted_phone_number,rating,review",
            "key": GOOGLE_MAPS_API
        }
        response = requests.get(url, params=params)
        place_details = response.json()["result"]
        
        # Create a dictionary representing the location and add it to the list
        location_dict = {
            "name": place_details["name"],
            "address": place_details["formatted_address"],
            #"phone_number": place_details.get("formatted_phone_number", "N/A"),
            #"rating": place_details.get("rating", "N/A"),
            "reviews": []
        }
        
        # Add the top 3 reviews to the dictionary
        reviews = place_details.get("reviews", [])
        for review in reviews[:3]:
            review_dict = {
                #"author": review["author_name"],
                #"rating": review["rating"],
                "text": review["text"],
                #"time": review["relative_time_description"]
            }
            location_dict["reviews"].append(review_dict)
        
        locations.append(location_dict)
    
    return locations

# Define the function to be used in the Gradio app
def find_competitors(product, location):
    locations = get_location_data(product, location)
    if len(locations) == 0:
        return f"No competitors found for {product} in {location}."
    
    output_str = f"Top competitors for {product} in {location}:"
    for i, loc in enumerate(locations):
        output_str += f"\n{i+1}. {loc['name']}"
        output_str += f"\nAddress: {loc['address']}"
        #output_str += f"\nPhone number: {loc['phone_number']}"
        #output_str += f"\nRating: {loc['rating']}"
        output_str += f"\nTop 3 reviews:"
        for review in loc['reviews']:
            output_str += f"\n- {review['text']}"
            #output_str += f"\n  Author: {review['author']}"
            #output_str += f"\n  Rating: {review['rating']}"
            #output_str += f"\n  Time: {review['time']}"
            
    output_str2 = f"Top competitors for {product} in {location}:"
    for i, loc in enumerate(locations):
        output_str2 += f"\n{i+1}. {loc['name']}"
        output_str2 += f"\nAddress: {loc['address']}"
    
    #return output_str

    prompt_input = '''

    You are an expert management consultant that rivals the best of Mckinsey, Bain, BCG.
    The client wants to sell {} in {}.

    {}

    Provide an analysis of the following:

    - From the competition and reviews about its products and come up with creative insights to recommend the client execute as part of a differentiating business strategy.
    - From there, think step by step, explain 5 strategies in bullet points of a creative and effective business plan.
    - Suggest a location for the client and explain the rationale of this location step by step.

    '''.format(product, location, output_str)

   
    template = '''
    {history}
    {human_input}
    '''
    prompt = PromptTemplate(
        input_variables=["history", "human_input"], 
        template=template
    )

    chatgpt_chain = LLMChain(
        llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0.5,openai_api_key=OPENAI_API_KEY), 
        prompt=prompt, 
        verbose=True, 
        memory=ConversationBufferWindowMemory(k=10),
    )

    output = output_str2 + "\n\n" + chatgpt_chain.predict(human_input=prompt_input)
    
    return(output)

# Create the Gradio app interface
inputs = [
    gr.inputs.Textbox(label="Product to sell"),
    gr.inputs.Textbox(label="Location")
]


output = gr.outputs.Textbox(label="Business Plan")

interface = gr.Interface(fn=find_competitors, inputs=inputs, outputs=output, title="Auto Consultant", 
             description="Enter a product and a location to find competitors.")
interface.launch()