Update app.py

app.py

@@ -1,5 +1,204 @@
-# app.py
-from company_info_search import run_gradio_interface
+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()