Update utils.py

utils.py

@@ -16,8 +16,8 @@ import torch
 import random
 
 class DialogueItem(BaseModel):
-    speaker: Literal["Jane", "John"]  
-    display_speaker: str = "Jane"      
+    speaker: Literal["Jane", "John"]
+    display_speaker: str = "Jane"
     text: str
 
 class Dialogue(BaseModel):
@@ -96,9 +96,9 @@ def query_llm_for_additional_info(topic: str, existing_text: str) -> str:
     print(additional_info)
     return additional_info
 
-## NEW: Updated research_topic that uses Tavily and Open Deep Research Agent
+## Updated research_topic: Uses Tavily API for search/extraction and then GROQ API for generating the report.
 def research_topic(topic: str) -> str:
-    print("[LOG] Researching topic using Tavily API and Open Deep Research Agent:", topic)
+    print("[LOG] Researching topic using Tavily API and GROQ LLM:", topic)
     tavily_api_key = os.environ.get("TAVILY_API_KEY")
     if not tavily_api_key:
         print("[ERROR] TAVILY_API_KEY not found in environment variables.")
@@ -164,30 +164,36 @@ def research_topic(topic: str) -> str:
         "Make sure to include all relevant and updated information."
     )
     
-    deepseek_headers = {
-        "Authorization": f"Bearer {os.environ.get('DEEPSEEK_API_KEY')}",
+    # Use GROQ API to generate the report
+    groq_api_key = os.environ.get("GROQ_API_KEY")
+    if not groq_api_key:
+        print("[ERROR] GROQ_API_KEY not found in environment variables.")
+        return "GROQ API key not configured."
+    
+    groq_headers = {
+        "Authorization": f"Bearer {groq_api_key}",
         "Content-Type": "application/json"
     }
-    deepseek_data = {
-        "model": "deepseek/deepseek-r1",
+    groq_data = {
+        "model": "DeepSeek-R1-Distill-Llama-70B",
         "messages": [{"role": "user", "content": research_prompt}],
         "max_tokens": 4096,
         "temperature": 0.6
     }
     try:
-        deepseek_response = requests.post("https://openrouter.ai/api/v1/chat/completions",
-                                          headers=deepseek_headers, data=json.dumps(deepseek_data))
-        deepseek_response.raise_for_status()
-        response_json = deepseek_response.json()
+        groq_response = requests.post("https://api.groq.com/openai/v1/chat/completions",
+                                      headers=groq_headers, json=groq_data)
+        groq_response.raise_for_status()
+        response_json = groq_response.json()
         report_content = response_json["choices"][0]["message"]["content"].strip()
-        print("[LOG] Research report generated successfully.")
+        print("[LOG] Research report generated successfully using GROQ API.")
     except Exception as e:
-        print("[ERROR] Open Deep Research Agent API error:", e)
+        print("[ERROR] GROQ API error during research report generation:", e)
         report_content = f"Error generating research report: {str(e)}"
     
     return report_content
 
-## NEW: Function to generate a PDF report from text using wkhtmltopdf.
+## Function to generate a PDF report from text using wkhtmltopdf.
 def generate_pdf_report(report_text: str) -> str:
     """
     Generate a PDF file from the given report text using wkhtmltopdf.
@@ -195,8 +201,8 @@ def generate_pdf_report(report_text: str) -> str:
     """
     import subprocess
     import tempfile
-    
-    # Perform the newline replacement outside the f-string to avoid backslash issues.
+
+    # Replace newlines with HTML line breaks.
     converted_text = report_text.replace("\n", "<br>")
     
     html_content = f"""
@@ -217,7 +223,7 @@ def generate_pdf_report(report_text: str) -> str:
     with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as html_file:
         html_file.write(html_content.encode("utf-8"))
         html_path = html_file.name
-    
+
     pdf_path = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf").name
     try:
         subprocess.run(["wkhtmltopdf", html_path, pdf_path], check=True)
@@ -227,7 +233,7 @@ def generate_pdf_report(report_text: str) -> str:
         pdf_path = ""
     finally:
         os.remove(html_path)
-    
+
     return pdf_path
 
 def fetch_wikipedia_summary(topic: str) -> str: