Update app.py

app.py

@@ -2,6 +2,10 @@ import os
 import requests
 import gradio as gr
 from openai import OpenAI
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
 
 # Fetch API keys from environment variables
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
@@ -14,12 +18,16 @@ def fetch_linkedin_data(linkedin_url):
     headers = {'Authorization': f'Bearer {api_key}'}
     api_endpoint = 'https://nubela.co/proxycurl/api/v2/linkedin'
     
+    logging.info("Fetching LinkedIn data...")
     response = requests.get(api_endpoint,
                             params={'url': linkedin_url},
-                            headers=headers)
+                            headers=headers,
+                            timeout=10)  # Adding a timeout for safety
     if response.status_code == 200:
+        logging.info("LinkedIn data fetched successfully.")
         return response.json()
     else:
+        logging.error(f"Error fetching LinkedIn data: {response.text}")
         return {"error": f"Error fetching LinkedIn data: {response.text}"}
 
 # Function to fetch company information using Firecrawl API
@@ -39,10 +47,13 @@ def fetch_company_info(company_url):
         }
     }
     
-    response = requests.post(api_endpoint, json=data, headers=headers)
+    logging.info("Fetching company information...")
+    response = requests.post(api_endpoint, json=data, headers=headers, timeout=15)  # Adding a timeout for safety
     if response.status_code == 200:
+        logging.info("Company information fetched successfully.")
         return response.json()
     else:
+        logging.error(f"Error fetching company information: {response.text}")
         return {"error": f"Error fetching company information: {response.text}"}
 
 # Function to structure the email using the "Start with Why" model
@@ -53,31 +64,31 @@ def structure_email(user_data, linkedin_info, company_info):
     structured_input = f"{why}\n\n{how}\n\n{what}"
     return structured_input
 
-# Function to generate email content using Nvidia Nemotron LLM
+# Function to generate email content using Nvidia Nemotron LLM (non-streaming for simplicity)
 def generate_email_content(api_key, prompt):
     client = OpenAI(
         base_url="https://integrate.api.nvidia.com/v1",
         api_key=api_key
     )
     
-    completion = client.chat.completions.create(
-        model="nvidia/llama-3.1-nemotron-70b-instruct",
-        messages=[
-            {"role": "user", "content": prompt}
-        ],
-        temperature=0.5,
-        top_p=1,
-        max_tokens=1024,
-        stream=True
-    )
-
-    # Collect the streamed response
-    response_text = ""
-    for chunk in completion:
-        if chunk.choices[0].delta.content is not None:
-            response_text += chunk.choices[0].delta.content
-
-    return response_text
+    logging.info("Generating email content...")
+    try:
+        response = client.chat.completions.create(
+            model="nvidia/llama-3.1-nemotron-70b-instruct",
+            messages=[
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.5,
+            top_p=1,
+            max_tokens=1024,
+            stream=False  # Disable streaming for simplicity
+        )
+        email_content = response['choices'][0]['message']['content']
+        logging.info("Email content generated successfully.")
+        return email_content
+    except Exception as e:
+        logging.error(f"Error generating email content: {e}")
+        return "Error generating email content."
 
 # Function to validate the generated email for professional tone and completeness
 def validate_email(email_content):