company description using DuckDuckGo

Abandoned the LLM solution for generating a company description. Now using DuckDuckGo's Instant Answer API

app.py

@@ -6,7 +6,6 @@ import yaml
 from tools.final_answer import FinalAnswerTool
 from PIL import Image
 from io import BytesIO
-from transformers import pipeline
 
 from Gradio_UI import GradioUI
 
@@ -43,27 +42,26 @@ def company_logos(company_name: str) -> Image.Image:
 
 @tool
 def company_description(company_name: str) -> str:
-    """Generates a short company description using an LLM.
+    """Fetches a company description using DuckDuckGo's Instant Answer API.
 
-    Args: 
+    Args:
         company_name: The name of the company.
 
     Returns:
         A short description of the company.
     """
-
-    prompt = (
-        f"Provide a short summary about {company_name}. Include:\n"
-        "- One sentence describing the company's main product or service.\n"
-        "- Two sentences summarizing the company's history.\n"
-        "- One sentence about the latest news related to the company."
-    )
+    url = f"https://api.duckduckgo.com/?q={company_name}+company&format=json"
 
     try:
-        response = llm(prompt, max_length=150, do_sample=True)
-        return response[0]["generated_text"]
+        response = requests.get(url)
+        if response.status_code == 200:
+            data = response.json()
+            return data.get("AbstractText", "No description available.")
+        else:
+            return "Company description not found."
     except Exception as e:
-        return f"Error generating description for {company_name}: {str(e)}"
+        return f"Error fetching company description: {str(e)}"
+        
     
 final_answer = FinalAnswerTool()
 
@@ -78,8 +76,8 @@ custom_role_conversions=None,
 )
 
 
-# Import tool from Hub
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+# # Import tool from Hub
+# image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
 
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)