Update llm_models.py

llm_models.py

@@ -1,78 +1,78 @@
-import streamlit as st
-import requests
-from langchain.llms.base import LLM
-from typing import Optional, List
-import os
-from execution_programs import *
-
-# API key for llm model
-GROQ_API_KEY = os.environ.get("GROQ_TOKENS")
-class GroqLLM(LLM):
-    model: str = "qwen-2.5-coder-32b"
-    temperature: float = 0.3
-    api_key: str = GROQ_API_KEY
-
-    @property
-    def _llm_type(self) -> str:
-        return "groq-llm"
-
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json",
-        }
-
-        payload = {
-            "messages": [{"role": "user", "content": prompt}],
-            "model": self.model,
-            "temperature": self.temperature,
-        }
-
-        try:
-            response = requests.post(
-                "https://api.groq.com/openai/v1/chat/completions",
-                headers=headers,
-                json=payload
-            )
-            response.raise_for_status()
-            return response.json()["choices"][0]["message"]["content"]
-        except Exception as e:
-            st.error(f"Error calling Groq API: {str(e)}")
-            return "Sorry, there was an error processing your request."
-
-class GroqTextLLM(LLM):
-    model: str = "deepseek-r1-distill-llama-70b"
-    temperature: float = 0.7
-    api_key: str = GROQ_API_KEY
-
-    @property
-    def _llm_type(self) -> str:
-        return "groq-text-llm"
-
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json",
-        }
-
-        payload = {
-            "messages": [{"role": "user", "content": prompt}],
-            "model": self.model,
-            "temperature": self.temperature,
-        }
-
-        try:
-            response = requests.post(
-                "https://api.groq.com/openai/v1/chat/completions",
-                headers=headers,
-                json=payload
-            )
-            response.raise_for_status()
-            return response.json()["choices"][0]["message"]["content"]
-        except Exception as e:
-            st.error(f"Error calling Groq API: {str(e)}")
-            return "Sorry, there was an error processing your request."
-
-# Initialize LLMs
-code_llm = GroqLLM()
+import streamlit as st
+import requests
+from langchain.llms.base import LLM
+from typing import Optional, List
+import os
+from execution_programs import *
+
+# API key for llm model
+GROQ_API_KEY = os.environ.get("GROQ_TOKENS")
+class GroqLLM(LLM):
+    model: str = "llama-3.3-70b-versatile"
+    temperature: float = 0.3
+    api_key: str = GROQ_API_KEY
+
+    @property
+    def _llm_type(self) -> str:
+        return "groq-llm"
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+
+        payload = {
+            "messages": [{"role": "user", "content": prompt}],
+            "model": self.model,
+            "temperature": self.temperature,
+        }
+
+        try:
+            response = requests.post(
+                "https://api.groq.com/openai/v1/chat/completions",
+                headers=headers,
+                json=payload
+            )
+            response.raise_for_status()
+            return response.json()["choices"][0]["message"]["content"]
+        except Exception as e:
+            st.error(f"Error calling Groq API: {str(e)}")
+            return "Sorry, there was an error processing your request."
+
+class GroqTextLLM(LLM):
+    model: str = "deepseek-r1-distill-llama-70b"
+    temperature: float = 0.7
+    api_key: str = GROQ_API_KEY
+
+    @property
+    def _llm_type(self) -> str:
+        return "groq-text-llm"
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+
+        payload = {
+            "messages": [{"role": "user", "content": prompt}],
+            "model": self.model,
+            "temperature": self.temperature,
+        }
+
+        try:
+            response = requests.post(
+                "https://api.groq.com/openai/v1/chat/completions",
+                headers=headers,
+                json=payload
+            )
+            response.raise_for_status()
+            return response.json()["choices"][0]["message"]["content"]
+        except Exception as e:
+            st.error(f"Error calling Groq API: {str(e)}")
+            return "Sorry, there was an error processing your request."
+
+# Initialize LLMs
+code_llm = GroqLLM()
 text_llm = GroqTextLLM()