Update app.py

app.py

@@ -12,7 +12,6 @@ import tempfile
 # 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"
 HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_KEY")
 
 # Set Hugging Face API token
@@ -31,11 +30,14 @@ LANGUAGE_CODES = {
 def speech_to_text(audio_file, language_code):
     """Convert uploaded audio file to text"""
     recognizer = sr.Recognizer()
+
     try:
+        # Save uploaded file to temporary location
         with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
             tmp_file.write(audio_file.getvalue())
             audio_path = tmp_file.name
 
+        # Convert to WAV if necessary
         if not audio_path.endswith('.wav'):
             audio = AudioSegment.from_file(audio_path)
             wav_path = audio_path + ".wav"
@@ -69,15 +71,22 @@ def load_knowledge_base():
         return None
 
 def setup_agents(language='en'):
+    """Setup CrewAI agents with the correct LLM provider"""
+    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"
+    }
+
     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={
-            "model": "HuggingFaceH4/zephyr-7b-beta",
-            "provider": "huggingface"
-        },
+        llm={"model": "HuggingFaceH4/zephyr-7b-beta", "provider": "huggingface"},
         llm_kwargs={
             "temperature": 0.4,
             "max_length": 512
@@ -90,10 +99,7 @@ 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={
-            "model": "HuggingFaceH4/zephyr-7b-beta",
-            "provider": "huggingface"
-        },
+        llm={"model": "HuggingFaceH4/zephyr-7b-beta", "provider": "huggingface"},
         llm_kwargs={
             "temperature": 0.5,
             "max_length": 612
@@ -104,6 +110,7 @@ def setup_agents(language='en'):
     return researcher, educator
 
 def process_question(question, target_lang='en'):
+    """Process the user's question using AI agents"""
     try:
         nasa_data = get_nasa_data()
         vector_store = load_knowledge_base()
@@ -114,7 +121,8 @@ def process_question(question, target_lang='en'):
             NASA Context: {nasa_data.get('explanation', '')}
             Language: {target_lang}""",
             agent=researcher,
-            expected_output="3 verified technical points"
+            expected_output="3 verified technical points",
+            output_file="research.md"
         )
 
         explain_task = Task(
@@ -135,12 +143,14 @@ def process_question(question, target_lang='en'):
         return f"Error: {str(e)}"
 
 # Streamlit Interface
-st.title("🚀 COSMOLAB (Multilingual Space Agent)")
+st.title("🚀COSMOLAB (Multilingual Space Agent)")
 st.markdown("### Ask space questions in any language!")
 
+# Single language selection for both input and output
 selected_lang = st.selectbox("Select Language", list(LANGUAGE_CODES.keys()))
-lang_code = LANGUAGE_CODES[selected_lang].split('-')[0]
+lang_code = LANGUAGE_CODES[selected_lang].split('-')[0]  # Extract base language code
 
+# Input Method
 input_method = st.radio("Input Method", ["Text", "Audio File"])
 
 question = ""
@@ -148,6 +158,7 @@ if input_method == "Text":
     question = st.text_input(f"Your space question in {selected_lang}:", "")
 else:
     audio_file = st.file_uploader("Upload audio file", type=["wav", "mp3", "ogg"])
+
     if audio_file is not None:
         with st.spinner("Processing audio..."):
             question = speech_to_text(audio_file, LANGUAGE_CODES[selected_lang])