Update app.py

app.py

@@ -8,13 +8,17 @@ import os
 import speech_recognition as sr
 from pydub import AudioSegment
 import tempfile
+import litellm
 
 # Configuration
 NASA_API_URL = "https://api.nasa.gov/planetary/apod?api_key=DEMO_KEY"
-HF_MODEL_NAME = "all-MiniLM-L6-v2"
-LLM_REPO = "HuggingFaceH4/zephyr-7b-beta"
+HF_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+LLM_MODEL = "HuggingFaceH4/zephyr-7b-beta"
 HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_KEY")
 
+# Set Hugging Face API token for litellm
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACE_API_TOKEN  
+
 # Language Configuration
 LANGUAGE_CODES = {
     'English': 'en-US',
@@ -25,13 +29,9 @@ LANGUAGE_CODES = {
     'Arabic': 'ar-SA'
 }
 
-# Set Hugging Face API token in environment
-os.environ["HUGGINGFACE_API_KEY"] = HUGGINGFACE_API_TOKEN
-
 def speech_to_text(audio_file, language_code):
     """Convert uploaded audio file to text"""
     recognizer = sr.Recognizer()
-
     try:
         with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
             tmp_file.write(audio_file.getvalue())
@@ -70,27 +70,27 @@ def load_knowledge_base():
     except Exception as e:
         return None
 
-def setup_agents(language='en'):
-    prompts = {
-        'en': "Explain space concepts clearly in English",
-        'es': "Explica conceptos espaciales en español",
-        'fr': "Expliquez les concepts spatiaux en français",
-        'de': "Erklären Sie Raumfahrtkonzepte auf Deutsch",
-        'zh': "用中文清楚解释空间概念",
-        'ar': "اشرح مفاهيم الفضاء باللغة العربية"
-    }
+def call_llm(prompt):
+    """Correctly call Hugging Face models using litellm"""
+    try:
+        response = litellm.completion(
+            model="huggingface",
+            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}
+        )
+        return response["choices"][0]["message"]["content"]
+    except Exception as e:
+        return f"LLM Error: {str(e)}"
 
+def setup_agents(language='en'):
     researcher = Agent(
         role="Multilingual Space Analyst",
         goal="Analyze and validate space information",
         backstory="Expert in multilingual space data analysis with NASA mission experience.",
         verbose=True,
-        llm={"provider": "huggingface", "model": LLM_REPO},  # ✅ Explicit provider fix
-        llm_kwargs={
-            "temperature": 0.4,
-            "max_length": 512
-        },
-        memory=True
+        llm=call_llm  # ✅ Now correctly calls Hugging Face using litellm
     )
 
     educator = Agent(
@@ -98,12 +98,7 @@ 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={"provider": "huggingface", "model": LLM_REPO},  # ✅ Explicit provider fix
-        llm_kwargs={
-            "temperature": 0.5,
-            "max_length": 612
-        },
-        memory=True
+        llm=call_llm  # ✅ Now correctly calls Hugging Face using litellm
     )
 
     return researcher, educator