company_info_search.py

import gradio as gr
from swarm import Swarm, Agent
from openai import OpenAI
from exa_py import Exa
import os
from dotenv import load_dotenv
import tweepy
import json

load_dotenv()

openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
client = Swarm(client=openai_client)
exa_client = Exa(api_key=os.getenv("EXA_API_KEY"))

# Twitter API setup
bearer_token = os.getenv('bearer_token')
twitter_client = tweepy.Client(bearer_token=bearer_token)

def search_prop_firm_info(query, num_results=5):
    results = exa_client.search_and_contents(
        query,
        type="keyword",
        num_results=int(num_results),
        text=True,
        start_published_date="2023-01-01",
        category="company",
        include_domains=["propfirmmatch.com"],
        summary=True
    )
    formatted_results = []
    for result in results.results:
        formatted_results.append(f"Title: {result.title}\nURL: {result.url}\nSummary: {result.summary}\n")
    return "\n".join(formatted_results)

def search_trustpilot_reviews(company_name, num_results=3):
    query = f"site:trustpilot.com {company_name} reviews"
    results = exa_client.search_and_contents(
        query,
        type="keyword",
        num_results=int(num_results),
        text=True,
        start_published_date="2023-01-01",
        summary=True
    )
    formatted_results = []
    for result in results.results:
        formatted_results.append(f"Title: {result.title}\nURL: {result.url}\nSummary: {result.summary}\n")
    return "\n".join(formatted_results)

def search_tweets(query, max_results=100):
    try:
        formatted_query = query.replace('"', '').strip()
        tweets = twitter_client.search_recent_tweets(query=formatted_query, max_results=max_results)
        return tweets.data if tweets.data else []
    except tweepy.errors.BadRequest as e:
        print(f"BadRequest error: {e}")
        return []
    except tweepy.errors.TweepyException as e:
        print(f"Tweepy error: {e}")
        return []
    except Exception as e:
        print(f"Unexpected error in search_tweets: {e}")
        return []

twitter_sentiment_agent = Agent(
    name="Twitter Sentiment Analyzer",
    instructions="""You are an agent that analyzes Twitter sentiment for proprietary trading firms. 
    Given a list of tweets about a specific firm, analyze the overall sentiment and provide a summary.
    Consider the following:
    1. Overall sentiment (positive, negative, or neutral)
    2. Common themes or topics mentioned
    3. Any notable praise or complaints
    4. Level of engagement (replies, retweets, likes)
    Provide a concise summary of your findings.""",
    functions=[search_tweets],
)

trustpilot_review_agent = Agent(
    name="TrustPilot Review Analyzer",
    instructions="""You are an agent that searches for and analyzes TrustPilot reviews for proprietary trading firms. 
    Use the search_trustpilot_reviews function to find reviews for a given company.
    Your tasks are to:
    1. Search for TrustPilot reviews using the provided company name.
    2. Analyze the overall sentiment of the reviews (positive, negative, or mixed).
    3. Identify common themes or recurring points in the reviews.
    4. Note any standout positive or negative comments.
    5. If available, mention the overall TrustPilot rating for the company.
    6. Provide a concise summary of your findings, highlighting the most important aspects for a potential trader.
    
    Your summary should be informative and balanced, presenting both positives and negatives if they exist.""",
    functions=[search_trustpilot_reviews],
)

prop_firm_search_agent = Agent(
    name="Prop Firm Search",
    instructions="""You are an agent that searches for proprietary trading firm information on propfirmmatch.com. Use the search_prop_firm_info function to find information based on the user's query. The function takes two parameters: query (string) and num_results (integer, default 5).

    If the user names a specific firm:
    1. Use the firm's name as the search query.
    2. Call the search_prop_firm_info function with the firm's name.
    3. Provide a detailed summary of the information found about the firm from propfirmmatch.com.

    Always include relevant details such as leverage, accepted countries, and any unique features of the firms.""",
    functions=[search_prop_firm_info],
)

score_agent = Agent(
    name="Score Generator",
    instructions="""You are an agent that generates an overall score for proprietary trading firms based on the information provided.
    Consider the following factors:
    1. Information from propfirmmatch.com
    2. TrustPilot reviews
    3. Twitter sentiment
    4. Any unique features or advantages of the firm
    
    Generate a score out of 100, where:
    90-100: Excellent
    80-89: Very Good
    70-79: Good
    60-69: Fair
    Below 60: Poor
    
    Provide a brief explanation for the score.
    
    Format your response as follows:
    **Score:** (your score here)
    **Twitter:** (Highlights of twitter analysis here)
    **TrustPilot:** (Highlights / notable reviews here)
    **PropFirmMatch:** (Summary of firm from search_propfirm_info / propfirmmatch agent)""",
    functions=[],
)

def fetch_twitter_sentiment(company_name, num_tweets=100):
    query = f"{company_name} -is:retweet"
    tweets = search_tweets(query, max_results=num_tweets)
    
    if not tweets:
        return "No tweets found."
    
    tweet_texts = [tweet.text for tweet in tweets]
    tweet_data = "\n\n".join(tweet_texts)
    
    analysis_prompt = f"Analyze the sentiment of the following tweets about {company_name}:\n\n{tweet_data}"
    
    sentiment_response = client.run(twitter_sentiment_agent, messages=[{"role": "user", "content": analysis_prompt}])
    sentiment_analysis = sentiment_response.messages[-1]["content"] if sentiment_response.messages else "No sentiment analysis available."
    
    return sentiment_analysis

def search_prop_firms(query):
    search_response = client.run(prop_firm_search_agent, messages=[{"role": "user", "content": query}])
    search_results = search_response.messages[-1]["content"] if search_response.messages else "No search results."
    
    trustpilot_analysis = get_trustpilot_analysis(query)
    twitter_sentiment = fetch_twitter_sentiment(query)
    
    combined_results = f"{search_results}\n\nTrustPilot Analysis:\n{trustpilot_analysis}\n\nTwitter Sentiment Analysis:\n{twitter_sentiment}"
    
    score_prompt = f"Generate a score for {query} based on the following information:\n\n{combined_results}"
    score_response = client.run(score_agent, messages=[{"role": "user", "content": score_prompt}])
    score_result = score_response.messages[-1]["content"] if score_response.messages else "No score available."
    
    return query, search_results, trustpilot_analysis, twitter_sentiment, score_result

def get_trustpilot_analysis(firm_name):
    trustpilot_prompt = f"Find and analyze TrustPilot reviews for {firm_name}"
    trustpilot_response = client.run(trustpilot_review_agent, messages=[{"role": "user", "content": trustpilot_prompt}])
    return trustpilot_response.messages[-1]["content"] if trustpilot_response.messages else "No TrustPilot analysis available."

def format_report_card(query):
    firm_name, search_results, trustpilot_analysis, twitter_sentiment, score_result = search_prop_firms(query)
    
    report_card = {
        "firm_name": firm_name,
        "overall_score": score_result,
        "firm_info": search_results,
        "trustpilot_analysis": trustpilot_analysis,
        "twitter_sentiment": twitter_sentiment
    }
    
    return json.dumps(report_card)

def run_gradio_interface():
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("# Proprietary Trading Firm Analysis")
        
        with gr.Row():
            with gr.Column(scale=2):
                query_input = gr.Textbox(label="Enter firm name", lines=2)
                search_button = gr.Button("Generate Report", variant="primary")
            with gr.Column(scale=1):
                gr.Markdown("### Example Queries")
                gr.Examples(
                    examples=["FTMO", "MyForexFunds", "The5ers"],
                    inputs=query_input
                )
        
        output = gr.JSON(label="Report Card")
        
        search_button.click(format_report_card, inputs=[query_input], outputs=[output])

    demo.launch(share=True)

if __name__ == "__main__":
    run_gradio_interface()