Update utils.py

utils.py

@@ -26,20 +26,32 @@ def generate_script(prompt, text, tone, length, host_name, guest_name, sponsor_s
     Calls the LLM to generate a structured podcast script from research text.
     """
     groq_client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-    response = groq_client.chat.completions.create(
-        messages=[
-            {"role": "system", "content": prompt},
-            {"role": "user", "content": text}
-        ],
-        model="DeepSeek-R1-Distill-Llama-70B",
-        max_tokens=4096,
-        temperature=0.6
-    )
 
-    script_content = response.choices[0].message.content.strip()
-    dialogue_items = parse_script_to_dialogue(script_content, host_name, guest_name)
-    
-    return Dialogue(dialogue=dialogue_items)
+    try:
+        response = groq_client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": prompt},
+                {"role": "user", "content": text}
+            ],
+            model="DeepSeek-R1-Distill-Llama-70B",
+            max_tokens=4096,
+            temperature=0.6
+        )
+
+        if not response.choices or not response.choices[0].message.content:
+            raise ValueError("LLM returned an empty response.")
+
+        script_content = response.choices[0].message.content.strip()
+        dialogue_items = parse_script_to_dialogue(script_content, host_name, guest_name)
+
+        if not dialogue_items:
+            raise ValueError("Script parsing failed. No valid dialogue found.")
+
+        return Dialogue(dialogue=dialogue_items)
+
+    except Exception as e:
+        print(f"[ERROR] Failed to generate script: {str(e)}")
+        return Dialogue(dialogue=[DialogueItem(speaker="Jane", display_speaker="Jane", text="I'm sorry, something went wrong.")])
 
 def parse_script_to_dialogue(script, host_name, guest_name):
     """
@@ -63,7 +75,7 @@ def truncate_text(text, max_tokens=2048):
     tokenizer = tiktoken.get_encoding("cl100k_base")
     tokens = tokenizer.encode(text)
 
-    if len(tokens) > max_tokens:  # ✅ Fixed condition
+    if len(tokens) > max_tokens:
         return tokenizer.decode(tokens[:max_tokens])
     
     return text