Create app.py

app.py

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+
+import gradio as gr
+from langchain_community.vectorstores import Qdrant
+from qdrant_client import QdrantClient
+from qdrant_client.http import models
+from google.colab import userdata
+from qdrant_client import QdrantClient
+from langchain_openai import OpenAIEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_openai import ChatOpenAI
+from google.colab import userdata
+from typing import Optional
+
+def util_bot(question: str, openai_api_key: str) -> Optional[str]:
+    """
+    Processes a given question using a combination of Qdrant vector search and an LLM (Large Language Model) response generation.
+
+    This function embeds the input question using OpenAI's embedding model, searches for relevant context using Qdrant,
+    and generates a response based on the context found and the input question using an LLM.
+
+    Args:
+        question (str): The user's question to be answered.
+        openai_api_key (str): API key for accessing OpenAI's services.
+
+    Returns:
+        Optional[str]: The generated answer, or None if no answer could be generated.
+
+    Raises:
+        Exception: If an error occurs during processing.
+    """
+
+    # Configuration for Qdrant client
+    qdrant_end =os.environ['Qdrant_embeddings_url'] 
+    qdrant_api_key = os.environ['Qdrant_api_key']
+
+    # Initialize Qdrant client
+    qdrant_client = QdrantClient(url=qdrant_end, api_key=qdrant_api_key)
+
+    # Initialize embeddings using OpenAI
+    embeddings = OpenAIEmbeddings(model='text-embedding-3-small',openai_api_key=os.environ['openai_api_key'])
+
+    try:
+        # Embed the input question for vector search
+        query_result = embeddings.embed_query(question)
+
+        # Perform vector search in the "util-bot" collection
+        response = qdrant_client.search(
+            collection_name="util-bot",
+            query_vector=query_result,
+            limit=3  # Retrieve top 3 closest vectors
+        )
+
+        # Construct the prompt template for the LLM
+        prompt=PromptTemplate(
+        template=""""Use the following pieces of context to answer the questions at the end.If
+        you don't know the answer, just say don't know. do not try to make up the answer.
+
+        
+        {context}
+        
+
+        Question: {question}
+        Helpful Answer,formatted in markdown:""",
+        input_variables=["context","question"]
+        )
+
+        # Initialize LLM and the chain for generating the response
+        llm = ChatOpenAI(model='gpt-3.5-turbo-0125',openai_api_key=os.environ['openai_api_key'])
+        chain = LLMChain(llm=llm, prompt=prompt)
+
+        # Generate the response
+        result = chain({
+            "question": question,
+            "context": "\n".join([doc.payload['page_content'] for doc in response])  # Concatenate context from search results
+        })
+
+        return result['text']
+    except Exception as e:
+        # Log the exception or handle it as per the application's error handling policy
+        print(f"Error processing question: {e}")
+        return None
+
+
+with gr.Blocks() as app:
+    # Customize the app's appearance
+    gr.Markdown("Utility Bill Helper")
+    gr.Markdown("### Ask any question related to Utility bills in the USA")
+
+    with gr.Row():
+        question = gr.Textbox(lines=3, label="Enter your question", placeholder="Type your question here...")
+        
+
+    submit_button = gr.Button("Submit")
+
+    with gr.Column():
+        response = gr.Textbox(label="Response", placeholder="The response will appear here...", lines=10, interactive=False)
+
+    # Define the action to take on button click - Ensure util_bot function is awaited if it's async
+    submit_button.click(util_bot, inputs=[question, openai_api_key], outputs=response)
+
+    # Customizing the app's theme and adding CSS for fonts and colors
+    app.css = """
+    body { font-family: 'Arial', sans-serif; }
+    h2 { font-weight: bold; }
+    .gr-button { background-color: #4CAF50; color: white; font-size: 16px; }
+    .gr-textbox { border-color: #4CAF50; }
+    .gr-textbox:focus { border-color: #66BB6A; box-shadow: 0 0 0 0.2rem rgba(102, 187, 106, 0.25); }
+    """
+
+app.launch()