Update app.py

app.py

@@ -1,18 +1,100 @@
 import os
+from dotenv import load_dotenv
+import gradio as gr
+from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
+from llama_index.llms.huggingface import HuggingFaceInferenceAPI
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from sentence_transformers import SentenceTransformer
+from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
+load_dotenv()
+# Configure the Llama index settings
+Settings.llm = HuggingFaceInferenceAPI(
+    model_name="google/gemma-1.1-7b-it",
+    tokenizer_name="google/gemma-1.1-7b-it",
+    context_window=3000,
+    token=os.getenv("HF_TOKEN"),
+    max_new_tokens=512,
+    generate_kwargs={"temperature": 0.1},
+)
+Settings.embed_model = HuggingFaceEmbedding(
+    model_name="BAAI/bge-small-en-v1.5"
+)
 
-# Define the paths for the directories
-db_dir = 'db'
-data_dir = 'data'
-
-# Check if the directories already exist
-if not os.path.exists(db_dir):
-    os.makedirs(db_dir)
-    print(f"Directory '{db_dir}' created successfully.")
-else:
-    print(f"Directory '{db_dir}' already exists.")
-
-if not os.path.exists(data_dir):
-    os.makedirs(data_dir)
-    print(f"Directory '{data_dir}' created successfully.")
-else:
-    print(f"Directory '{data_dir}' already exists.")
+# Define the directory for persistent storage and data
+PERSIST_DIR = "db"
+PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs
+
+# Ensure PDF directory exists
+os.makedirs(PDF_DIRECTORY, exist_ok=True)
+os.makedirs(PERSIST_DIR, exist_ok=True)
+
+def data_ingestion_from_directory():
+    # Use SimpleDirectoryReader on the directory containing the PDF files
+    documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
+    storage_context = StorageContext.from_defaults()
+    index = VectorStoreIndex.from_documents(documents)
+    index.storage_context.persist(persist_dir=PERSIST_DIR)
+
+def handle_query(query):
+    chat_text_qa_msgs = [
+        (
+            "user",
+            """
+            You are a Q&A assistant named RedfernsTech, created by the RedfernsTech team. You have been designed to provide accurate answers based on the context provided.
+            Context:
+            {context_str}
+            Question:
+            {query_str}
+            """
+        )
+    ]
+    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
+
+    # Load index from storage
+    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
+    index = load_index_from_storage(storage_context)
+
+    query_engine = index.as_query_engine(text_qa_template=text_qa_template)
+    answer = query_engine.query(query)
+
+    if hasattr(answer, 'response'):
+        return answer.response
+    elif isinstance(answer, dict) and 'response' in answer:
+        return answer['response']
+    else:
+        return "Sorry, I couldn't find an answer."
+
+# Example usage
+
+    # Process PDF ingestion from directory
+print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
+data_ingestion_from_directory()
+
+    # Example query
+query = "How do I use the RedfernsTech Q&A assistant?"
+print("Query:", query)
+response = handle_query(query)
+print("Answer:", response)
+# prompt: create a gradio chatbot for this
+
+
+
+# Define the input and output components for the Gradio interface
+input_component = gr.Textbox(
+    show_label=False,
+    placeholder="Ask me anything about the document..."
+)
+
+output_component = gr.Textbox()
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=handle_query,
+    inputs=input_component,
+    outputs=output_component,
+    title="RedfernsTech Q&A Chatbot",
+    description="Ask me anything about the uploaded document."
+)
+
+# Launch the Gradio interface
+interface.launch(server_port=7861, share=True)