Upload 2 files

app.py

@@ -5,12 +5,13 @@ import pickle
 import gradio as gr
 import numpy as np
 import pandas as pd
-from google import genai
+from openai import OpenAI
 
 MODEL_PATH = "random_forest_regression.pkl"
 
-LLM_API_KEY = os.getenv("GOOGLE_API_KEY", "")
-LLM_MODEL = os.getenv("LLM_MODEL", "gemini-2.0-flash-lite")
+# DeepInfra API credentials
+LLM_API_KEY = os.getenv("DEEPINFRA_API_KEY", "")
+LLM_MODEL = os.getenv("LLM_MODEL", "meta-llama/Llama-3.3-70B-Instruct-Turbo")
 
 with open(MODEL_PATH, "rb") as model_file:
     model = pickle.load(model_file)
@@ -41,17 +42,20 @@ def match_town(user_town: str):
 
 def call_llm_json(system_prompt: str, user_prompt: str) -> str:
     if not LLM_API_KEY:
-        raise ValueError("GOOGLE_API_KEY environment variable is not set.")
-    client = genai.Client(api_key=LLM_API_KEY)
-    response = client.models.generate_content(
+        raise ValueError("DEEPINFRA_API_KEY environment variable is not set.")
+    client = OpenAI(
+        api_key=LLM_API_KEY,
+        base_url="https://api.deepinfra.com/v1/openai",
+    )
+    response = client.chat.completions.create(
         model=LLM_MODEL,
-        contents=user_prompt,
-        config={
-            "system_instruction": system_prompt,
-            "max_output_tokens": 512,
-        },
+        max_tokens=512,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
     )
-    return response.text
+    return response.choices[0].message.content
 
 
 def parse_json_response(raw: str, required_keys: tuple) -> dict:

requirements.txt

@@ -1,4 +1,4 @@
-google-genai>=1.0.0
+openai>=1.0.0
 gradio>=4.0.0
 scikit-learn==1.6.1
 numpy