Update app.py

app.py

@@ -105,45 +105,55 @@ Reference json:
 }
 """
 
-def ask_llm(user_message, model='llama-3.3-70b-versatile', system_prompt="You are a helpful assistant."):
-    client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-    response = client.chat.completions.create(
-        model=model,
-        messages=[
+# def chat_groq(user_message, model='llama-3.3-70b-versatile', system_prompt="You are a helpful assistant."):
+#     client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+#     response = client.chat.completions.create(
+#         model=model,
+#         messages=[
+#             {
+#                 "role": "system",
+#                 "content": system_prompt
+#             },
+#             {
+#                 "role": "user", 
+#                 "content": user_message
+#             }
+#         ],
+#         stream=False,
+#     )
+
+#     return response.choices[0].message.content
+
+def chat_groq(user_message, model='llama-3.3-70b-versatile', system_prompt="You are a helpful assistant."):
+    payload = {
+        "models": [model],
+        "messages": [
             {
                 "role": "system",
                 "content": system_prompt
             },
             {
-                "role": "user", 
+                "role": "user",
                 "content": user_message
             }
-        ],
-        stream=False,
-    )
+        ]
+    }
 
-    return response.choices[0].message.content
+    response = requests.post("https://organizedprogrammers-bettergroqinterface.hf.space/chat", json=payload)
 
-def ask_ollama(user_message, model='llama-3.3-70b-versatile', system_prompt=search_prompt):
-    client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+    if response.status_code == 200:
+        return response.json()
+    else:
+        raise Exception(f"API request failed with status {response.status_code}: {response.text}")
 
-    response = client.chat.completions.create(
-        model=model,
-        messages=[
-            {
-                "role": "system",
-                "content": system_prompt
-            },
-            {
-                "role": "user", 
-                "content": user_message
-            }
-        ],
-        stream=False,
-    )
-    
-    ai_reply = response.choices[0].message.content
+    return response['content'][0]['message']['content']
+
+def ask_llm(user_message, model='llama-3.3-70b-versatile', system_prompt="You are a helpful assistant."):
+    return chat_groq(user_message, model=model, system_prompt=system_prompt)
+
+def ask_ollama(user_message, model='llama-3.3-70b-versatile', system_prompt=search_prompt):
+    ai_reply = chat_groq(user_message, model=model, system_prompt=system_prompt)
     print(f"AI REPLY json:\n{ai_reply}")
 
     # Process the response to ensure we return valid JSON