Create app.py

app.py

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+import gradio as gr
+from llama_index import VectorStoreIndex, SimpleDirectoryReader
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from llama_index import LangchainEmbedding, ServiceContext
+from llama_index import StorageContext, load_index_from_storage
+from llama_index import LLMPredictor
+from langchain import HuggingFaceHub
+from pathlib import Path
+import random
+import string
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+repo_id = os.getenv("repo_id")
+model_name = os.getenv("model_name")
+
+def generate_random_string(length):
+    letters = string.ascii_lowercase
+    return ''.join(random.choice(letters) for i in range(length))
+
+def process_documents_and_query(pdf_files, question):
+    random_string = generate_random_string(20)
+    directory_path = random_string
+    os.makedirs(directory_path)
+
+    documents = []
+    for pdf_file in pdf_files:
+        file_path = os.path.join(directory_path, pdf_file.name)
+        with open(file_path, 'wb') as f:
+            f.write(pdf_file.read())
+        documents.append(file_path)
+
+    embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name=model_name))
+
+    llm = HuggingFaceHub(repo_id=repo_id,
+                         model_kwargs={"min_length": 1024, "max_new_tokens": 5632,
+                                       "do_sample": True, "temperature": 0.1,
+                                       "top_k": 50, "top_p": 0.95, "eos_token_id": 49155})
+
+    llm_predictor = LLMPredictor(llm)
+
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
+
+    new_index = VectorStoreIndex.from_documents(documents, service_context=service_context)
+
+    if question:
+        new_index.storage_context.persist("directory_path")
+        storage_context = StorageContext.from_defaults(persist_dir="directory_path")
+        loadedindex = load_index_from_storage(storage_context=storage_context, service_context=service_context)
+        query_engine = loadedindex.as_query_engine()
+        initial_response = query_engine.query(question)
+        return str(initial_response)
+
+    return "Please enter a question."
+
+gr_interface = gr.Interface(
+    fn=process_documents_and_query,
+    inputs=[gr.inputs.File(label="Upload PDF files", type="file", accept=".pdf", multiple=True), gr.inputs.Textbox(label="Enter your query here:")],
+    outputs=gr.outputs.Textbox(label="AI Response"),
+    title="AI Doc-Chat",
+    description="Upload PDF files and ask questions!",
+    allow_flagging="never"
+)
+
+gr_interface.launch()