Update app.py

app.py

@@ -13,7 +13,7 @@ import litellm
 # Configuration
 NASA_API_URL = "https://api.nasa.gov/planetary/apod?api_key=DEMO_KEY"
 HF_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
-LLM_MODEL = "HuggingFaceH4/zephyr-7b-beta"
+LLM_MODEL = "huggingface/HuggingFaceH4/zephyr-7b-beta"
 HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_KEY")
 
 # Set Hugging Face API token for litellm
@@ -74,11 +74,9 @@ def call_llm(prompt):
     """Correctly call Hugging Face models using litellm"""
     try:
         response = litellm.completion(
-            model="huggingface",
+            model=LLM_MODEL,
             messages=[{"role": "user", "content": prompt}],
-            api_base="https://api-inference.huggingface.co/models",
-            api_key=HUGGINGFACE_API_TOKEN,
-            model_kwargs={"model": LLM_MODEL, "temperature": 0.4}
+            api_key=HUGGINGFACE_API_TOKEN
         )
         return response["choices"][0]["message"]["content"]
     except Exception as e:
@@ -90,7 +88,7 @@ def setup_agents(language='en'):
         goal="Analyze and validate space information",
         backstory="Expert in multilingual space data analysis with NASA mission experience.",
         verbose=True,
-        llm=call_llm  # ✅ Now correctly calls Hugging Face using litellm
+        llm="huggingface/HuggingFaceH4/zephyr-7b-beta"  # ✅ Now correctly set as a model string
     )
 
     educator = Agent(
@@ -98,7 +96,7 @@ def setup_agents(language='en'):
         goal=f"Explain complex concepts in {language} using simple terms",
         backstory=f"Multilingual science communicator specializing in {language} explanations.",
         verbose=True,
-        llm=call_llm  # ✅ Now correctly calls Hugging Face using litellm
+        llm="huggingface/HuggingFaceH4/zephyr-7b-beta"  # ✅ Now correctly set as a model string
     )
 
     return researcher, educator