Create app.py

app.py

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+import streamlit as st
+from crewai import Agent, Task, Crew
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import WebBaseLoader
+import requests
+import os
+import speech_recognition as sr
+from pydub import AudioSegment
+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")
+
+# Language Configuration
+LANGUAGE_CODES = {
+    'English': 'en-US',
+    'Spanish': 'es-ES',
+    'French': 'fr-FR',
+    'German': 'de-DE',
+    'Chinese': 'zh-CN',
+    '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:
+        # Save uploaded file to temporary location
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") 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"
+            audio.export(wav_path, format="wav")
+            audio_path = wav_path
+
+        with sr.AudioFile(audio_path) as source:
+            audio_data = recognizer.record(source)
+            return recognizer.recognize_google(audio_data, language=language_code)
+    
+    except Exception as e:
+        st.error(f"Audio processing error: {str(e)}")
+        return ""
+    finally:
+        if os.path.exists(audio_path):
+            os.remove(audio_path)
+
+def get_nasa_data():
+    try:
+        return requests.get(NASA_API_URL, timeout=10).json()
+    except Exception as e:
+        return {"error": f"NASA API Error: {str(e)}"}
+
+def load_knowledge_base():
+    try:
+        loader = WebBaseLoader(["https://mars.nasa.gov/news/"])
+        docs = loader.load()[:3]
+        embeddings = HuggingFaceEmbeddings(model_name=HF_MODEL_NAME)
+        return FAISS.from_documents(docs, embeddings)
+    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"
+    }
+
+    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=LLM_REPO,
+        llm_kwargs={
+            "temperature": 0.4,
+            "max_length": 512
+        },
+        memory=True
+    )
+
+    educator = Agent(
+        role="Bilingual Science Educator",
+        goal=f"Explain complex concepts in {language} using simple terms",
+        backstory=f"Multilingual science communicator specializing in {language} explanations.",
+        verbose=True,
+        llm=LLM_REPO,
+        llm_kwargs={
+            "temperature": 0.5,
+            "max_length": 612
+        },
+        memory=True
+    )
+
+    return researcher, educator
+
+def process_question(question, target_lang='en'):
+    try:
+        nasa_data = get_nasa_data()
+        vector_store = load_knowledge_base()
+        researcher, educator = setup_agents(target_lang)
+
+        research_task = Task(
+            description=f"""Research: {question}
+            NASA Context: {nasa_data.get('explanation', '')}
+            Language: {target_lang}""",
+            agent=researcher,
+            expected_output="3 verified technical points",
+            output_file="research.md"
+        )
+
+        explain_task = Task(
+            description=f"Explain in {target_lang} using simple terms and analogies",
+            agent=educator,
+            expected_output="2-paragraph answer in requested language",
+            context=[research_task]
+        )
+
+        crew = Crew(
+            agents=[researcher, educator],
+            tasks=[research_task, explain_task],
+            verbose=True
+        )
+
+        return crew.kickoff()
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Streamlit Interface
+st.title("🚀 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]  # Extract base language code
+
+# Input Method
+input_method = st.radio("Input Method", ["Text", "Audio File"])
+
+question = ""
+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])
+            if question:
+                st.text_area("Transcribed Text", value=question, height=100)
+
+if question:
+    with st.spinner("Analyzing with AI agents..."):
+        answer = process_question(question, lang_code)
+        st.markdown(f"### 🌍 Answer ({selected_lang}):")
+        st.markdown(answer)
+
+st.markdown("---")
+st.markdown("*Powered by NASA API & Open Source AI*")