completed code upto CRM

ai_agent_tutorials/ai_lead_generation_agent/main.py

@@ -0,0 +1,120 @@
+import requests
+from phi.agent import Agent
+from phi.tools.firecrawl import FirecrawlTools
+from phi.model.openai import OpenAIChat
+
+# Step 1: Search for relevant URLs using Firecrawl
+def search_for_urls():
+    url = "https://api.firecrawl.dev/v1/search"
+    headers = {
+        "Authorization": "Bearer fc-",  # Replace with your Firecrawl API key
+        "Content-Type": "application/json"
+    }
+    company_description = "voice cloning open source models or api"
+    query1 = f"quora websites where people are looking for {company_description} services"
+    payload = {
+        "query": query1,
+        "limit": 3,  # Adjust the limit as needed
+        "lang": "en",
+        "location": "United States",
+        "timeout": 60000,
+    }
+    response = requests.post(url, json=payload, headers=headers)
+    if response.status_code == 200:
+        data = response.json()
+        if data.get("success"):
+            results = data.get("data", [])
+            return [result["url"] for result in results]
+        else:
+            print("Search request was not successful.")
+            print(data.get("warning", "No warning provided."))
+    else:
+        print(f"Failed to retrieve data. Status code: {response.status_code}")
+    return []
+
+# Step 2: Set up the Firecrawl Agent
+firecrawl_tools = FirecrawlTools(
+    api_key="fc-",  # Replace with your Firecrawl API key
+    scrape=True,    # Enable scraping to extract detailed content
+    crawl=True,     # Enable crawling
+    limit=5         # Limit the number of pages to crawl
+)
+
+# First Agent: Crawls the website and retrieves content
+firecrawl_agent = Agent(
+    model=OpenAIChat(id="gpt-4o-mini", api_key="sk-proj-"),  # Replace with your OpenAI API key
+    tools=[firecrawl_tools],  # Add Firecrawl tools
+    show_tool_calls=False,    # Disable verbose tool call output
+    markdown=True
+)
+
+# Second Agent: Processes verbose output and extracts user information
+extraction_agent = Agent(
+    model=OpenAIChat(id="gpt-4o-mini", api_key="sk-proj-"),  # Replace with your OpenAI API key
+    show_tool_calls=False,
+    system_prompt="You are an expert at extracting user information from responses. You are given a long response of extracted and crawled website information and you need to extract the user information only. Focus on extracting information about both the question asker and the answerers, as they are potential leads.",
+    markdown=True
+)
+
+# Step 3: Define a function to analyze user info directly from URLs
+def analyze_user_info_from_urls(urls):
+    user_info_list = []
+    for website_url in urls:
+        print(f"Analyzing website: {website_url}")
+        
+        # Step 3.1: Directly instruct the first agent to crawl and analyze the website
+        analysis_prompt = (
+            f"Visit the website {website_url} using Firecrawl and analyze its content to extract the following information:\n"
+            "1. Username of the person asking the question.\n"
+            "2. The content of their question.\n"
+            "3. Usernames of people answering the question.\n"
+            "4. The content of their answers.\n"
+            "5. Any additional relevant information (e.g., timestamp, upvotes, links, user bio, location).\n\n"
+            "Return the extracted information in a structured format."
+        )
+        
+        # Step 3.2: Run the first agent with the analysis prompt
+        analysis_response = firecrawl_agent.run(analysis_prompt)
+        analysis_result = analysis_response.content
+        
+        # Step 3.3: Use the second agent to extract only the user information
+        extraction_prompt = (
+            f"Extract only the following user information from the content below:\n"
+            "1. Username (for both question asker and answerers)\n"
+            "2. Post content (question or answer)\n"
+            "3. Timestamp\n"
+            "4. Upvotes\n"
+            "5. Links (if available)\n"
+            "6. Any additional relevant information (e.g., user bio, location)\n\n"
+            f"Content:\n{analysis_result}"
+        )
+        extraction_response = extraction_agent.run(extraction_prompt)
+        extracted_info = extraction_response.content
+        
+        # Step 3.4: Store the results
+        user_info_list.append({
+            "website_url": website_url,
+            "user_info": extracted_info
+        })
+    return user_info_list
+
+# Step 4: Main workflow
+def main():
+    # Step 4.1: Search for relevant URLs
+    urls = search_for_urls()
+    print("Relevant URLs Found:")
+    for url in urls:
+        print(url)
+
+    # Step 4.2: Analyze user info directly from the URLs
+    user_info_list = analyze_user_info_from_urls(urls)
+
+    # Step 4.3: Print the extracted information in a detailed format
+    print("\nExtracted User Information:")
+    for info in user_info_list:
+        print(f"Website: {info['website_url']}")
+        print(f"User Info:\n{info['user_info']}\n")
+
+# Run the main workflow
+if __name__ == "__main__":
+    main()

ai_agent_tutorials/ai_lead_generation_agent/test.py

@@ -14,8 +14,6 @@ company_name = "Rollout AI"
 company_description = "voice cloning open source models or api"
 keywords = "https://rollout.site"
 
-# Craft the query
-query = f" website blogs, forums, and reddit communities URL to {company_description}, where i can find people looking for similar services"
 query1 = f" reddit and quora websites where people are looking for {company_description} services"
 
 # Set the payload
@@ -45,3 +43,24 @@ if response.status_code == 200:
 else:
     print(f"Failed to retrieve data. Status code: {response.status_code}")
 
+
+
+    firecrawl_tools = FirecrawlTools(
+        api_key=st.session_state.firecrawl_api_key,
+        scrape=False,
+        crawl=True,
+        limit=5
+    )
+
+    firecrawl_agent = Agent(
+        model=OpenAIChat(id="gpt-4o-mini", api_key=st.session_state.openai_api_key),
+        tools=[firecrawl_tools, DuckDuckGo()],
+        show_tool_calls=True,
+        markdown=True
+    )
+
+    analysis_agent = Agent(
+        model=OpenAIChat(id="gpt-4o-mini", api_key=st.session_state.openai_api_key),
+        show_tool_calls=True,
+        markdown=True
+    )