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import gradio as gr
from huggingface_hub import InferenceApi
from duckduckgo_search import DDGS
import requests
import json
from typing import List
from pydantic import BaseModel, Field
# Global variables
huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")
# Function to perform a DuckDuckGo search
def duckduckgo_search(query):
with DDGS() as ddgs:
results = ddgs.text(query, max_results=5)
return results
class CitingSources(BaseModel):
sources: List[str] = Field(
...,
description="List of sources to cite. Should be an URL of the source."
)
def get_response_with_search(query):
# Perform the web search
search_results = duckduckgo_search(query)
# Use the search results as context for the model
context = "\n".join(f"{result['title']}\n{result['body']}\nSource: {result['href']}\n"
for result in search_results if 'body' in result)
# Prompt formatted for Mistral-7B-Instruct
prompt = f"""<s>[INST] Using the following context:
{context}
Write a detailed and complete research document that fulfills the following user request: '{query}'
After writing the document, please provide a list of sources used in your response. [/INST]"""
# API endpoint for Mistral-7B-Instruct-v0.3
API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
# Headers
headers = {"Authorization": f"Bearer {huggingface_token}"}
# Payload
payload = {
"inputs": prompt,
"parameters": {
"max_new_tokens": 1000,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"repetition_penalty": 1.1
}
}
# Make the API call
response = requests.post(API_URL, headers=headers, json=payload)
if response.status_code == 200:
result = response.json()
if isinstance(result, list) and len(result) > 0:
generated_text = result[0].get('generated_text', 'No text generated')
# Remove the instruction part
content_start = generated_text.find("[/INST]")
if content_start != -1:
generated_text = generated_text[content_start + 7:].strip()
# Split the response into main content and sources
parts = generated_text.split("Sources:", 1)
main_content = parts[0].strip()
sources = parts[1].strip() if len(parts) > 1 else ""
return main_content, sources
else:
return f"Unexpected response format: {result}", ""
else:
return f"Error: API returned status code {response.status_code}", ""
def gradio_interface(query):
main_content, sources = get_response_with_search(query)
formatted_response = f"{main_content}\n\nSources:\n{sources}"
return formatted_response
# Gradio interface
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
outputs="text",
title="AI-powered Web Search Assistant",
description="Ask a question, and I'll search the web and provide an answer using the Mistral-7B-Instruct model.",
examples=[
["Latest news about Yann LeCun"],
["Latest news site:github.blog"],
["Where I can find best hotel in Galapagos, Ecuador intitle:hotel"],
["filetype:pdf intitle:python"]
]
)
if __name__ == "__main__":
iface.launch() |