Create app.py

app.py

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+import requests
+from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, ServiceContext, PromptHelper
+import gradio as gr
+
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Get Hugging Face API key from environment variable
+hf_api_key = os.getenv("HF_API_KEY")
+
+API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
+headers = {"Authorization": f"Bearer {hf_api_key}"}
+
+
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
+def init_index(directory_path):
+    # Model parameters
+    max_input_size = 4096
+    num_outputs = 512
+    max_chunk_overlap = 20
+    chunk_size_limit = 600
+
+    # Prompt helper and predictor
+    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
+    llm_predictor = LLMPredictor(llm=query)
+
+    # Read documents from the "docs" folder
+    documents = SimpleDirectoryReader(directory_path).load_data()
+
+    # Initialize index with document data
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
+    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
+
+    # Save the created index
+    index.save_to_disk('index.json')
+
+    return index
+
+def chatbot(input_text):
+    # Load index
+    index = GPTSimpleVectorIndex.load_from_disk('index.json')
+
+    # Get response for the question
+    response = index.query(input_text, response_mode="compact")
+
+    return response.response
+
+# Create index
+init_index("docs")
+
+# Create UI interface to interact with the Hugging Face model
+iface = gr.Interface(fn=chatbot,
+                     inputs=gr.components.Textbox(lines=7, placeholder="Enter your question here"),
+                     outputs="text",
+                     title="Frost AI ChatBot: Your Knowledge Companion Powered by Hugging Face",
+                     description="Ask any question about rahasak research papers",
+                     allow_screenshot=True)
+iface.launch(share=True)