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app.py

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+import streamlit as st
+import requests
+from agno.agent import Agent
+from agno.tools.firecrawl import FirecrawlTools
+from agno.models.groq import Groq
+from firecrawl import FirecrawlApp
+from pydantic import BaseModel, Field
+from typing import List
+from composio_agno import Action, ComposioToolSet
+import json
+
+class QuoraUserInteractionSchema(BaseModel):
+    username: str = Field(description="The username of the user who posted the question or answer")
+    bio: str = Field(description="The bio or description of the user")
+    post_type: str = Field(description="The type of post, either 'question' or 'answer'")
+    timestamp: str = Field(description="When the question or answer was posted")
+    upvotes: int = Field(default=0, description="Number of upvotes received")
+    links: List[str] = Field(default_factory=list, description="Any links included in the post")
+
+class QuoraPageSchema(BaseModel):
+    interactions: List[QuoraUserInteractionSchema] = Field(description="List of all user interactions (questions and answers) on the page")
+
+def search_for_urls(company_description: str, firecrawl_api_key: str, num_links: int) -> List[str]:
+    url = "https://api.firecrawl.dev/v1/search"
+    headers = {
+        "Authorization": f"Bearer {firecrawl_api_key}",
+        "Content-Type": "application/json"
+    }
+    query1 = f"quora websites where people are looking for {company_description} services"
+    payload = {
+        "query": query1,
+        "limit": num_links,
+        "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]
+    return []
+
+def extract_user_info_from_urls(urls: List[str], firecrawl_api_key: str) -> List[dict]:
+    user_info_list = []
+    firecrawl_app = FirecrawlApp(api_key=firecrawl_api_key)
+    
+    try:
+        for url in urls:
+            response = firecrawl_app.extract(
+                [url],
+                {
+                    'prompt': 'Extract all user information including username, bio, post type (question/answer), timestamp, upvotes, and any links from Quora posts. Focus on identifying potential leads who are asking questions or providing answers related to the topic.',
+                    'schema': QuoraPageSchema.model_json_schema(),
+                }
+            )
+            
+            if response.get('success') and response.get('status') == 'completed':
+                interactions = response.get('data', {}).get('interactions', [])
+                if interactions:
+                    user_info_list.append({
+                        "website_url": url,
+                        "user_info": interactions
+                    })
+    except Exception:
+        pass
+    
+    return user_info_list
+
+def format_user_info_to_flattened_json(user_info_list: List[dict]) -> List[dict]:
+    flattened_data = []
+    
+    for info in user_info_list:
+        website_url = info["website_url"]
+        user_info = info["user_info"]
+        
+        for interaction in user_info:
+            flattened_interaction = {
+                "Website URL": website_url,
+                "Username": interaction.get("username", ""),
+                "Bio": interaction.get("bio", ""),
+                "Post Type": interaction.get("post_type", ""),
+                "Timestamp": interaction.get("timestamp", ""),
+                "Upvotes": interaction.get("upvotes", 0),
+                "Links": ", ".join(interaction.get("links", [])),
+            }
+            flattened_data.append(flattened_interaction)
+    
+    return flattened_data
+
+def create_google_sheets_agent(composio_api_key: str, groq_api_key: str) -> Agent:
+    composio_toolset = ComposioToolSet(api_key=composio_api_key)
+    google_sheets_tool = composio_toolset.get_tools(actions=[Action.GOOGLESHEETS_SHEET_FROM_JSON])[0]
+    
+    google_sheets_agent = Agent(
+        model=Groq(id="llama-3.3-70b-versatile",api_key=groq_api_key),
+        tools=[google_sheets_tool],
+        show_tool_calls=True,
+        markdown=True
+    )
+    
+    # Set the system prompt after creating the agent
+    google_sheets_agent.system_prompt = (
+        "You are an expert at creating and updating Google Sheets. "
+        "You will be given user information in JSON format, and you need to write it into a new Google Sheet."
+    )
+    
+    return google_sheets_agent
+
+def write_to_google_sheets(flattened_data: List[dict], composio_api_key: str, groq_api_key: str) -> str:
+    google_sheets_agent = create_google_sheets_agent(composio_api_key, groq_api_key)
+    
+    try:
+        message = (
+            "Create a new Google Sheet with this data. "
+            "The sheet should have these columns: Website URL, Username, Bio, Post Type, Timestamp, Upvotes, and Links in the same order as mentioned. "
+            "Here's the data in JSON format:\n\n"
+            f"{json.dumps(flattened_data, indent=2)}"
+        )
+        
+        create_sheet_response = google_sheets_agent.run(message)
+        print("Google Sheets Agent Response:", create_sheet_response.content)
+
+        if "https://docs.google.com/spreadsheets/d/" in create_sheet_response.content:
+            google_sheets_link = create_sheet_response.content.split("https://docs.google.com/spreadsheets/d/")[1].split(" ")[0]
+            return f"https://docs.google.com/spreadsheets/d/{google_sheets_link}"
+    except Exception:
+        pass
+    return None
+
+def create_prompt_transformation_agent(groq_api_key: str) -> Agent:
+    prompt_transformation_agent = Agent(
+        model=Groq(id="llama-3.3-70b-versatile",api_key=groq_api_key),
+        markdown=True
+    )
+    
+    # Set the system prompt after creating the agent
+    prompt_transformation_agent.system_prompt = (
+        "You are an expert at transforming detailed user queries into concise company descriptions. "
+        "Your task is to extract the core business/product focus in 3-4 words.\n\n"
+        "Examples:\n"
+        "Input: 'Generate leads looking for AI-powered customer support chatbots for e-commerce stores.'\n"
+        "Output: 'AI customer support chatbots for e commerce'\n\n"
+        "Input: 'Find people interested in voice cloning technology for creating audiobooks and podcasts'\n"
+        "Output: 'voice cloning technology'\n\n"
+        "Always focus on the core product/service and keep it concise but clear."
+    )
+    
+    return prompt_transformation_agent
+
+def main():
+    st.title("🎯 Lead Generation Agent")
+    st.info("This firecrawl powered agent helps you generate leads from Quora by searching for relevant posts and extracting user information.")
+
+    with st.sidebar:
+        st.header("API Keys")
+        firecrawl_api_key = st.text_input("Firecrawl API Key", type="password")
+        st.caption(" Get your Firecrawl API key from [Firecrawl's website](https://www.firecrawl.dev/app/api-keys)")
+        groq_api_key = st.text_input("GROQ API Key", type="password")
+        st.caption(" Get your GROQ API key from [GROQ's website](https://console.groq.com/login)")
+        composio_api_key = st.text_input("Composio API Key", type="password")
+        st.caption(" Get your Composio API key from [Composio's website](https://composio.ai)")
+        
+        num_links = st.number_input("Number of links to search", min_value=1, max_value=10, value=3)
+        
+        if st.button("Reset"):
+            st.session_state.clear()
+            st.experimental_rerun()
+
+    user_query = st.text_area(
+        "Describe what kind of leads you're looking for:",
+        placeholder="e.g., Looking for users who need automated video editing software with AI capabilities",
+        help="Be specific about the product/service and target audience. The AI will convert this into a focused search query."
+    )
+
+    if st.button("Generate Leads"):
+        if not all([firecrawl_api_key, groq_api_key, composio_api_key, user_query]):
+            st.error("Please fill in all the API keys and describe what leads you're looking for.")
+        else:
+            with st.spinner("Processing your query..."):
+                transform_agent = create_prompt_transformation_agent(groq_api_key)
+                company_description = transform_agent.run(f"Transform this query into a concise 3-4 word company description: {user_query}")
+                st.write("🎯 Searching for:", company_description.content)
+            
+            with st.spinner("Searching for relevant URLs..."):
+                urls = search_for_urls(company_description.content, firecrawl_api_key, num_links)
+            
+            if urls:
+                st.subheader("Quora Links Used:")
+                for url in urls:
+                    st.write(url)
+                
+                with st.spinner("Extracting user info from URLs..."):
+                    user_info_list = extract_user_info_from_urls(urls, firecrawl_api_key)
+                
+                with st.spinner("Formatting user info..."):
+                    flattened_data = format_user_info_to_flattened_json(user_info_list)
+                
+                with st.spinner("Writing to Google Sheets..."):
+                    google_sheets_link = write_to_google_sheets(flattened_data, composio_api_key, groq_api_key)
+                
+                if google_sheets_link:
+                    st.success("Lead generation and data writing to Google Sheets completed successfully!")
+                    st.subheader("Google Sheets Link:")
+                    st.markdown(f"[View Google Sheet]({google_sheets_link})")
+                else:
+                    st.error("Failed to retrieve the Google Sheets link.")
+            else:
+                st.warning("No relevant URLs found.")
+
+if __name__ == "__main__":
+    main()