feat: agentic approach

app.py

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+import streamlit as st
+import logging
+from services.model_handler import ModelHandler
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+class AutismResearchApp:
+    def __init__(self):
+        """Initialize the application components"""
+        self.model_handler = ModelHandler()
+    
+    def _setup_streamlit(self):
+        """Setup Streamlit UI components"""
+        st.image("https://images.unsplash.com/photo-1642370324000-f204b23aafe0?q=80&w=4072&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D")
+        st.title("🧩 Além do Espectro 🧠✨")
+        st.subheader("Tudo o que você precisa saber além dos rotulos e explorando a riqueza das neurodivergências")
+        st.markdown("""
+            Pergunte o que quiser e eu vou analisar os últimos artigos científicos e fornecer uma resposta baseada em evidências.
+        """)
+    
+    def run(self):
+        """Run the main application loop"""
+        self._setup_streamlit()
+        
+        # Initialize session state for papers
+        if 'papers' not in st.session_state:
+            st.session_state.papers = []
+        
+        # Get user query
+        col1, col2 = st.columns(2, vertical_alignment="bottom", gap="small")
+
+        query = col1.text_input("O que você precisa saber?")
+        if col2.button("Enviar"):
+            # Show status while processing
+            with st.status("Processando sua Pergunta...") as status:
+                status.write("🔍 Buscando papers de pesquisa relevantes...")
+                status.write("📚 Analisando papers de pesquisa...")
+                status.write("✍️ Gerando resposta...")
+                answer = self.model_handler.generate_answer(query)
+                
+                status.write("✨ Resposta gerada! Exibindo resultados...")
+
+            st.success("✅ Resposta gerada com base nos artigos de pesquisa encontrados.")
+
+            
+            st.markdown("### Resposta")
+            st.markdown(answer)
+
+def main():
+    app = AutismResearchApp()
+    app.run()
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+transformers>=4.36.2
+streamlit>=1.29.0
+--extra-index-url https://download.pytorch.org/whl/cpu
+accelerate>=0.26.0
+arxiv>=1.4.7
+python-dotenv>=1.0.0
+agno==1.0.6
+ollama>=0.4.7
+pypdf>=3.11.1
+watchdog>=2.3.1

services/__init__.py

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+

services/model_handler.py

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+import logging
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import streamlit as st
+from agno.agent import Agent
+from agno.models.ollama import Ollama
+from agno.tools.arxiv import ArxivTools
+from agno.tools.pubmed import PubmedTools
+
+MODEL_PATH = "meta-llama/Llama-3.2-1B"
+
+class ModelHandler:
+    def __init__(self):
+        """Initialize the model handler"""
+        self.model = None
+        self.tokenizer = None
+        self.translator = None
+        self.researcher = None
+        self.summarizer = None
+        self.presenter = None
+        self._initialize_model()
+    
+    def _initialize_model(self):
+        """Initialize model and tokenizer"""
+        self.model, self.tokenizer = self._load_model()
+        self.translator = Agent(
+            name="Translator",
+            role="You will translate the query to English",
+            model=Ollama(id="llama3.2:1b"),
+            goal="Translate to English",
+            instructions=[
+                "Translate the query to English"
+            ]
+        ) 
+        
+        self.researcher = Agent(
+            name="Researcher",
+            role="You are a research scholar who specializes in autism research.",
+            model=Ollama(id="llama3.2:1b"), 
+            tools=[ArxivTools(), PubmedTools()],
+            instructions=[
+                "You need to understand the context of the question to provide the best answer based on your tools."
+                "Be precise and provide just enough information to be useful",
+                "You must cite the sources used in your answer."
+                "You must create an accessible summary.",
+                "The content must be for people without autism knowledge.",
+                "Focus in the main findings of the paper taking in consideration the question.",
+                "The answer must be brief."
+            ],
+            show_tool_calls=True,
+        )
+        self.summarizer = Agent(
+            name="Summarizer",
+            role="You are a specialist in summarizing research papers for people without autism knowledge.",
+            model=Ollama(id="llama3.2:1b"), 
+            instructions=[
+                "You must provide just enough information to be useful",
+                "You must cite the sources used in your answer.",
+                "You must be clear and concise.",
+                "You must create an accessible summary.",
+                "The content must be for people without autism knowledge.",
+                "Focus in the main findings of the paper taking in consideration the question.",
+                "The answer must be brief."
+                "Remove everything related to the run itself like: 'Running: transfer_', just use plain text",
+                "You must use the language provided by the user to present the results.",
+                "Add references to the sources used in the answer.",
+                "Add emojis to make the presentation more interactive."
+                "Translaste the answer to Portuguese."
+            ],
+            show_tool_calls=True,
+            markdown=True,
+            add_references=True,
+        )
+        
+        self.presenter = Agent(
+            name="Presenter",
+            role="You are a professional researcher who presents the results of the research.",
+            model=Ollama(id="llama3.2:1b"),
+            instructions=[
+                "You are multilingual",
+                "You must present the results in a clear and concise manner.",
+                "Clenaup the presentation to make it more readable.",
+                "Remove unnecessary information.",
+                "Remove everything related to the run itself like: 'Running: transfer_', just use plain text",
+                "You must use the language provided by the user to present the results.",
+                "Add references to the sources used in the answer.",
+                "Add emojis to make the presentation more interactive."
+                "Translaste the answer to Portuguese."
+            ],
+            add_references=True,
+        )
+        
+    
+    @staticmethod
+    @st.cache_resource
+    @st.cache_data
+    def _load_model():
+        try:
+            tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+            model = AutoModelForCausalLM.from_pretrained(MODEL_PATH)
+            return model, tokenizer
+        except Exception as e:
+            logging.error(f"Error loading model: {str(e)}")
+            return None, None
+    
+    def generate_answer(self, query: str) -> str:
+        try:
+            translator = self.translator.run(query, stream=False)
+            logging.info(f"Translated query")
+            research = self.researcher.run(translator.content, stream=False)
+            logging.info(f"Generated research")
+            summary = self.summarizer.run(research.content, stream=False)
+            logging.info(f"Generated summary")
+            presentation = self.presenter.run(summary.content, stream=False)
+            logging.info(f"Generated presentation")
+            
+            if not presentation.content:
+                return self._get_fallback_response()
+            return presentation.content
+        except Exception as e:
+            logging.error(f"Error generating answer: {str(e)}")
+            return self._get_fallback_response()
+    
+    @staticmethod
+    def _get_fallback_response() -> str:
+        """Provide a friendly, helpful fallback response"""
+        return """
+            Peço descula, mas encontrei um erro ao gerar a resposta. Tente novamente ou refaça a sua pergunta.
+        """