Update app.py

app.py

@@ -1,13 +1,12 @@
 import os
 from dotenv import load_dotenv
 import gradio as gr
-from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
+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",
@@ -29,7 +28,6 @@ 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()
@@ -37,14 +35,13 @@ def data_ingestion_from_directory():
     index = VectorStoreIndex.from_documents(documents)
     index.storage_context.persist(persist_dir=PERSIST_DIR)
 
-
-def handle_query(query, history):
+def handle_query(query):
     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 some basic information first like name, email, company name.
+            and collect the some basic inforation first also name ,email ,company name
             {context_str}
             Question:
             {query_str}
@@ -61,32 +58,43 @@ def handle_query(query, history):
     answer = query_engine.query(query)
 
     if hasattr(answer, 'response'):
-        response = answer.response
+        return answer.response
     elif isinstance(answer, dict) and 'response' in answer:
-        response = answer['response']
+        return answer['response']
     else:
-        response = "Sorry, I couldn't find an answer."
-
-    history.append((query, response))
-    return response, history
-
+        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
+    # 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 chatbot interface with history
-chatbot = gr.ChatInterface(
+# 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 documents.",
-    cache_examples=True,  # Enable history caching
+    description="Ask me anything about the uploaded document."
 )
 
-chatbot.launch()
+# Launch the Gradio interface
+interface.launch()