import gradio as gr
import requests
import json
from decouple import Config

config = Config('.env')

def query_vectara(question):
    user_message = question

    # Read authentication parameters from the .env file
    CUSTOMER_ID = config('CUSTOMER_ID')
    CORPUS_ID = config('CORPUS_ID')
    API_KEY = config('API_KEY')

    # Define the headers
    api_key_header = {
        "customer-id": CUSTOMER_ID,
        "x-api-key": API_KEY
    }

    # Define the request body in the structure provided in the example
    request_body = {
        "query": [
            {
                "query": user_message,
                "queryContext": "",
                "start": 1,
                "numResults": 10,
                "contextConfig": {
                    "charsBefore": 0,
                    "charsAfter": 0,
                    "sentencesBefore": 2,
                    "sentencesAfter": 2,
                    "startTag": "%START_SNIPPET%",
                    "endTag": "%END_SNIPPET%",
                },
                "rerankingConfig": {
                    "rerankerId": 272725718,
                    "mmrConfig": {
                        "diversityBias": 0.27
                    }
                },
                "corpusKey": [
                    {
                        "customerId": CUSTOMER_ID,
                        "corpusId": CORPUS_ID,
                        "semantics": 0,
                        "metadataFilter": "",
                        "lexicalInterpolationConfig": {
                            "lambda": 0
                        },
                        "dim": []
                    }
                ],
                "summary": [
                    {
                        "maxSummarizedResults": 5,
                        "responseLang": "eng",
                        "summarizerPromptName": "vectara-summary-ext-v1.2.0"
                    }
                ]
            }
        ]
    }

    # Make the API request using Gradio
    response = requests.post(
        "https://api.vectara.io/v1/query",
        json=request_body,  # Use json to automatically serialize the request body
        verify=True,
        headers=api_key_header
    )


    if response.status_code == 200:  
        query_data = response.json()  
        print(query_data)    
        if query_data:  
            # Extract summary and the first 5 sources  
            response_set = query_data.get('responseSet', [{}])[0]  # get the first response set  
            summary = response_set.get('summary', [{}])[0]  # get the first summary  
            summary_text = summary.get('text', 'No summary available')  
            sources = response_set.get('response', [])[:5]  
            sources_text = [source.get('text', '') for source in sources]  
            return f"Summary: {summary_text}\n\nSources:\n{json.dumps(sources_text, indent=2)}"  
        else:  
            return "No data found in the response."  
    else:  
        return f"Error: {response.status_code}"  


iface = gr.Interface(
    fn=query_vectara,
    inputs=[gr.Textbox(label="Input Text")],
    outputs=gr.HTML(label="Output Text"),
    title="Vectara Chatbot",
    description="Ask me anything using the Vectara API!"
)

iface.launch()