Create app.py

app.py

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+import streamlit as st
+import logging
+import os
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import CSVLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.llms import HuggingFaceHub
+import dotenv
+import yaml
+
+# Load environment variables from .env file
+dotenv.load_dotenv()
+
+# Load configuration from YAML
+def load_config():
+    with open("yaml-editor-online.yaml", "r") as f:
+        config = yaml.safe_load(f)
+    return config
+
+import os
+# Securely fetch Hugging Face API key
+hf_token =  os.getenv('HUGGING') # Set this in your environment
+
+# Load configuration
+config = load_config()
+logging.basicConfig(level=logging.INFO)
+
+# Initialize Embeddings Model
+instructor_embeddings = HuggingFaceEmbeddings(model_name=config["embedding_model"])
+
+# Initialize FAISS Vector Database
+def create_vector_db():
+    try:
+        loader = CSVLoader(file_path="plant_biodata.csv", source_column="Information about the disease")
+        data = loader.load()
+        
+        # Create FAISS vector store
+        vectordb = FAISS.from_documents(documents=data, embedding=instructor_embeddings)
+        vectordb.save_local(config["vector_db_path"])
+        logging.info("Vector database successfully created and saved.")
+    except Exception as e:
+        logging.error("Error creating vector database:", exc_info=e)
+
+# Load FAISS and retrieve relevant documents
+import textwrap
+
+def get_qa_chain(query):
+    try:
+        if not os.path.exists(config["vector_db_path"]):
+            logging.error(f"FAISS index path does not exist: {config['vector_db_path']}")
+            return "Error: No data found."
+
+        vectordb = FAISS.load_local(
+            config["vector_db_path"], instructor_embeddings, allow_dangerous_deserialization=True
+        )
+        retriever = vectordb.as_retriever(score_threshold=config["score_threshold"])
+
+        # Retrieve top-k relevant documents
+        relevant_docs = retriever.get_relevant_documents(query)[:3]
+
+        if not relevant_docs:
+            return "No relevant information found."
+
+        # Summarizing relevant documents
+        summarized_context = " ".join(doc.page_content for doc in relevant_docs)
+        summarized_context = textwrap.shorten(summarized_context, width=600, placeholder="...")
+
+        # Refined prompt enforcing bullet-point output
+        prompt_template = """Given the following context and a question, generate a structured answer in bullet points.
+        QUESTION: {query}
+        
+        Ensure the response is simple, clear, and formatted as bullet points avoiding complex terms.
+        """
+        prompt = PromptTemplate(input_variables=["query"], template=prompt_template).format(query=query)
+
+        # Call LLM
+        llm = HuggingFaceHub(
+            repo_id=config["model_name"],
+            model_kwargs={
+                "temperature": config["temperature"],
+                "max_length": 150,
+                "top_p": config["top_p"],
+                "top_k": config["top_k"]
+            },
+            huggingfacehub_api_token=hf_token
+        )
+
+        response = llm(prompt)
+        return response.strip()
+
+    except Exception as e:
+        logging.error("Error getting response:", exc_info=e)
+        return "Sorry, there was an error processing your request."
+
+# Streamlit UI with Dark Mode
+def main():
+    st.set_page_config(page_title="Crop Disease Assistant", page_icon="🌱", layout="centered")
+    
+    # Custom CSS for Dark Mode
+    dark_theme_css = """
+    <style>
+    body {
+        background-color: #121212;
+        color: #ffffff;
+    }
+    .stTextInput, .stButton>button {
+        background-color: #1e1e1e;
+        color: white;
+        border-radius: 5px;
+        border: 1px solid #444;
+    }
+    .stMarkdown {
+        background-color: #1e1e1e;
+        padding: 10px;
+        border-radius: 5px;
+    }
+    </style>
+    """
+    st.markdown(dark_theme_css, unsafe_allow_html=True)
+    
+    st.title("🌾 Crop Disease Assistant")
+    st.write("Enter a crop disease-related question, and get simplified information.")
+
+    # Text input for the user query
+    query = st.text_input("Enter your query:")
+
+    # Display response on clicking the button
+    if st.button("Get Information"): 
+        if query:
+            response = get_qa_chain(query)
+            st.markdown(f"**📌 Response:**\n\n{response}")
+        else:
+            st.write("Please enter a query to get a response.")
+
+if __name__ == "__main__":
+    if not os.path.exists(config["vector_db_path"]):
+        logging.info(f"Vector database not found at {config['vector_db_path']}, creating it now.")
+        create_vector_db()
+    main()