Update app.py

app.py

@@ -1,12 +1,13 @@
 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.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
 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",
@@ -28,6 +29,7 @@ PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs
 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()
@@ -35,13 +37,14 @@ def data_ingestion_from_directory():
     index = VectorStoreIndex.from_documents(documents)
     index.storage_context.persist(persist_dir=PERSIST_DIR)
 
-def handle_query(query):
+
+def handle_query(query, history):
     chat_text_qa_msgs = [
         (
             "user",
             """
             You are a RedfernsTech chatbot whose aim is to provide better service to the user, utilizing provided context to deliver answers.
-            and collect the some basic inforation first also name ,email ,company name
+            and collect some basic information first like name, email, company name.
             {context_str}
             Question:
             {query_str}
@@ -58,43 +61,32 @@ def handle_query(query):
     answer = query_engine.query(query)
 
     if hasattr(answer, 'response'):
-        return answer.response
+        response = answer.response
     elif isinstance(answer, dict) and 'response' in answer:
-        return answer['response']
+        response = answer['response']
     else:
-        return "Sorry, I couldn't find an answer."
+        response = "Sorry, I couldn't find an answer."
 
-# Example usage
+    history.append((query, response))
+    return response, history
 
-    # Process PDF ingestion from directory
+
+# Example usage
 print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
 data_ingestion_from_directory()
 
-    # Example query
+# Example query
 query = "How do I use the RedfernsTech Q&A assistant?"
 print("Query:", query)
-response = handle_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(
+# Create the Gradio chatbot interface with history
+chatbot = gr.ChatInterface(
     fn=handle_query,
-    inputs=input_component,
-    outputs=output_component,
     title="RedfernsTech Q&A Chatbot",
-    description="Ask me anything about the uploaded document."
+    description="Ask me anything about the uploaded documents.",
+    cache_examples=True,  # Enable history caching
 )
 
-# Launch the Gradio interface
-interface.launch()
+chatbot.launch()