Shreyas094 commited on
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1 Parent(s): 04dbe4f

Update app.py

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  1. app.py +107 -356
app.py CHANGED
@@ -1,352 +1,146 @@
 
 
 
1
  import os
2
- import json
3
  import logging
4
- import shutil
5
- from tempfile import NamedTemporaryFile
6
- from typing import List
7
- from pydantic import BaseModel, Field
8
- from langchain_core.documents import Document
9
- from langchain_community.document_loaders import PyPDFLoader
10
- from langchain_community.vectorstores import FAISS
11
- from langchain_community.embeddings import HuggingFaceEmbeddings
12
- from llama_parse import LlamaParse
13
- import gradio as gr
14
 
15
- # Set up basic configuration for logging
16
- logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
17
 
18
  # Environment variables and configurations
19
  huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")
20
- llama_cloud_api_key = os.environ.get("LLAMA_CLOUD_API_KEY")
21
- ACCOUNT_ID = os.environ.get("CLOUDFARE_ACCOUNT_ID")
22
- API_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")
23
- API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/a17f03e0f049ccae0c15cdcf3b9737ce/ai/run/"
24
-
25
- print(f"ACCOUNT_ID: {ACCOUNT_ID}")
26
- print(f"CLOUDFLARE_AUTH_TOKEN: {API_TOKEN[:5]}..." if API_TOKEN else "Not set")
27
-
28
- MODELS = [
29
- "mistralai/Mistral-7B-Instruct-v0.3",
30
- "mistralai/Mixtral-8x7B-Instruct-v0.1",
31
- "@cf/meta/llama-3.1-8b-instruct",
32
- "mistralai/Mistral-Nemo-Instruct-2407"
33
- ]
34
-
35
- # Initialize LlamaParse
36
- llama_parser = LlamaParse(
37
- api_key=llama_cloud_api_key,
38
- result_type="markdown",
39
- num_workers=4,
40
- verbose=True,
41
- language="en",
42
- )
43
-
44
- def load_document(file: NamedTemporaryFile, parser: str = "llamaparse") -> List[Document]:
45
- """Loads and splits the document into pages."""
46
- if parser == "pypdf":
47
- loader = PyPDFLoader(file.name)
48
- return loader.load_and_split()
49
- elif parser == "llamaparse":
50
- try:
51
- documents = llama_parser.load_data(file.name)
52
- return [Document(page_content=doc.text, metadata={"source": file.name}) for doc in documents]
53
- except Exception as e:
54
- print(f"Error using Llama Parse: {str(e)}")
55
- print("Falling back to PyPDF parser")
56
- loader = PyPDFLoader(file.name)
57
- return loader.load_and_split()
58
- else:
59
- raise ValueError("Invalid parser specified. Use 'pypdf' or 'llamaparse'.")
60
-
61
- def get_embeddings():
62
- return HuggingFaceEmbeddings(model_name="avsolatorio/GIST-Embedding-v0")
63
-
64
- DOCUMENTS_FILE = "uploaded_documents.json"
65
 
66
- def load_documents():
67
- if os.path.exists(DOCUMENTS_FILE):
68
- with open(DOCUMENTS_FILE, "r") as f:
69
- return json.load(f)
70
- return []
71
 
72
- def save_documents(documents):
73
- with open(DOCUMENTS_FILE, "w") as f:
74
- json.dump(documents, f)
75
 
76
- # Replace the global uploaded_documents with this
77
- uploaded_documents = load_documents()
78
 
79
- def update_vectors(files, parser):
80
- global uploaded_documents
81
- logging.info(f"Entering update_vectors with {len(files)} files and parser: {parser}")
82
-
83
- if not files:
84
- logging.warning("No files provided for update_vectors")
85
- return "Please upload at least one PDF file.", display_documents()
86
-
87
- embed = get_embeddings()
88
- total_chunks = 0
89
-
90
- all_data = []
91
- for file in files:
92
- logging.info(f"Processing file: {file.name}")
93
- try:
94
- data = load_document(file, parser)
95
- if not data:
96
- logging.warning(f"No chunks loaded from {file.name}")
97
- continue
98
- logging.info(f"Loaded {len(data)} chunks from {file.name}")
99
- all_data.extend(data)
100
- total_chunks += len(data)
101
- if not any(doc["name"] == file.name for doc in uploaded_documents):
102
- uploaded_documents.append({"name": file.name, "selected": True})
103
- logging.info(f"Added new document to uploaded_documents: {file.name}")
104
- else:
105
- logging.info(f"Document already exists in uploaded_documents: {file.name}")
106
- except Exception as e:
107
- logging.error(f"Error processing file {file.name}: {str(e)}")
108
-
109
- logging.info(f"Total chunks processed: {total_chunks}")
110
-
111
- if not all_data:
112
- logging.warning("No valid data extracted from uploaded files")
113
- return "No valid data could be extracted from the uploaded files. Please check the file contents and try again.", display_documents()
114
-
115
  try:
116
- if os.path.exists("faiss_database"):
117
- logging.info("Updating existing FAISS database")
118
- database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
119
- database.add_documents(all_data)
120
- else:
121
- logging.info("Creating new FAISS database")
122
- database = FAISS.from_documents(all_data, embed)
123
-
124
- database.save_local("faiss_database")
125
- logging.info("FAISS database saved")
126
  except Exception as e:
127
- logging.error(f"Error updating FAISS database: {str(e)}")
128
- return f"Error updating vector store: {str(e)}", display_documents()
129
-
130
- # Save the updated list of documents
131
- save_documents(uploaded_documents)
132
-
133
- return f"Vector store updated successfully. Processed {total_chunks} chunks from {len(files)} files using {parser}.", display_documents()
134
-
135
- def delete_documents(selected_docs):
136
- global uploaded_documents
137
-
138
- if not selected_docs:
139
- return "No documents selected for deletion.", display_documents()
140
-
141
- embed = get_embeddings()
142
- database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
143
-
144
- deleted_docs = []
145
- docs_to_keep = []
146
- for doc in database.docstore._dict.values():
147
- if doc.metadata.get("source") not in selected_docs:
148
- docs_to_keep.append(doc)
149
- else:
150
- deleted_docs.append(doc.metadata.get("source", "Unknown"))
151
-
152
- # Print debugging information
153
- logging.info(f"Total documents before deletion: {len(database.docstore._dict)}")
154
- logging.info(f"Documents to keep: {len(docs_to_keep)}")
155
- logging.info(f"Documents to delete: {len(deleted_docs)}")
156
-
157
- if not docs_to_keep:
158
- # If all documents are deleted, remove the FAISS database directory
159
- if os.path.exists("faiss_database"):
160
- shutil.rmtree("faiss_database")
161
- logging.info("All documents deleted. Removed FAISS database directory.")
162
- else:
163
- # Create new FAISS index with remaining documents
164
- new_database = FAISS.from_documents(docs_to_keep, embed)
165
- new_database.save_local("faiss_database")
166
- logging.info(f"Created new FAISS index with {len(docs_to_keep)} documents.")
167
-
168
- # Update uploaded_documents list
169
- uploaded_documents = [doc for doc in uploaded_documents if doc["name"] not in deleted_docs]
170
- save_documents(uploaded_documents)
171
-
172
- return f"Deleted documents: {', '.join(deleted_docs)}", display_documents()
173
-
174
- def get_response_from_pdf(query, model, selected_docs, num_calls=3, temperature=0.2):
175
- logging.info(f"Entering get_response_from_pdf with query: {query}, model: {model}, selected_docs: {selected_docs}")
176
-
177
- embed = get_embeddings()
178
- if os.path.exists("faiss_database"):
179
- logging.info("Loading FAISS database")
180
- database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
181
- else:
182
- logging.warning("No FAISS database found")
183
- yield "No documents available. Please upload PDF documents to answer questions."
184
- return
185
-
186
- # Pre-filter the documents
187
- filtered_docs = []
188
- for doc_id, doc in database.docstore._dict.items():
189
- if isinstance(doc, Document) and doc.metadata.get("source") in selected_docs:
190
- filtered_docs.append(doc)
191
-
192
- logging.info(f"Number of documents after pre-filtering: {len(filtered_docs)}")
193
-
194
- if not filtered_docs:
195
- logging.warning(f"No documents found for the selected sources: {selected_docs}")
196
- yield "No relevant information found in the selected documents. Please try selecting different documents or rephrasing your query."
197
- return
198
-
199
- # Create a new FAISS index with only the selected documents
200
- filtered_db = FAISS.from_documents(filtered_docs, embed)
201
-
202
- retriever = filtered_db.as_retriever(search_kwargs={"k": 10})
203
- logging.info(f"Retrieving relevant documents for query: {query}")
204
- relevant_docs = retriever.get_relevant_documents(query)
205
- logging.info(f"Number of relevant documents retrieved: {len(relevant_docs)}")
206
-
207
- for doc in relevant_docs:
208
- logging.info(f"Document source: {doc.metadata['source']}")
209
- logging.info(f"Document content preview: {doc.page_content[:100]}...") # Log first 100 characters of each document
210
-
211
- context_str = "\n".join([doc.page_content for doc in relevant_docs])
212
- logging.info(f"Total context length: {len(context_str)}")
213
-
214
- if model == "@cf/meta/llama-3.1-8b-instruct":
215
- logging.info("Using Cloudflare API")
216
- # Use Cloudflare API with the retrieved context
217
- for response in get_response_from_cloudflare(prompt="", context=context_str, query=query, num_calls=num_calls, temperature=temperature, search_type="pdf"):
218
- yield response
219
- else:
220
- logging.info("Using Hugging Face API")
221
- # Use Hugging Face API
222
- prompt = f"""Using the following context from the PDF documents:
223
- {context_str}
224
- Write a detailed and complete response that answers the following user question: '{query}'"""
225
 
226
- client = InferenceClient(model, token=huggingface_token)
227
 
228
- response = ""
229
- for i in range(num_calls):
230
- logging.info(f"API call {i+1}/{num_calls}")
231
- for message in client.chat_completion(
232
- messages=[{"role": "user", "content": prompt}],
233
- max_tokens=10000,
234
- temperature=temperature,
235
- stream=True,
236
- ):
237
- if message.choices and message.choices[0].delta and message.choices[0].delta.content:
238
- chunk = message.choices[0].delta.content
239
- response += chunk
240
- yield response # Yield partial response
241
 
242
- logging.info("Finished generating response")
 
 
 
 
 
 
 
 
 
 
243
 
244
- def chatbot_interface(message, history, use_web_search, model, temperature, num_calls, selected_docs):
245
- if not message.strip():
246
- return "", history
 
 
 
247
 
248
- history = history + [(message, "")]
249
 
250
- try:
251
- if use_web_search:
252
- for response in get_response_with_search(message, model, num_calls=num_calls, temperature=temperature):
253
- history[-1] = (message, response)
254
- yield history
255
- else:
256
- for response in get_response_from_pdf(message, model, selected_docs, num_calls=num_calls, temperature=temperature):
257
- history[-1] = (message, response)
258
- yield history
259
- except gr.CancelledError:
260
- yield history
261
- except Exception as e:
262
- logging.error(f"Unexpected error in chatbot_interface: {str(e)}")
263
- history[-1] = (message, f"An unexpected error occurred: {str(e)}")
264
- yield history
265
 
266
- def retry_last_response(history, use_web_search, model, temperature, num_calls, selected_docs):
267
- if not history:
268
- return history
269
-
270
- last_user_msg = history[-1][0]
271
- history = history[:-1] # Remove the last response
272
-
273
- return chatbot_interface(last_user_msg, history, use_web_search, model, temperature, num_calls, selected_docs)
274
 
275
- def display_documents():
276
- return gr.CheckboxGroup(
277
- choices=[doc["name"] for doc in uploaded_documents],
278
- value=[doc["name"] for doc in uploaded_documents if doc["selected"]],
279
- label="Select documents to query or delete"
280
- )
281
 
282
- def initial_conversation():
283
- return [
284
- (None, "Welcome! I'm your AI assistant for web search and PDF analysis. Here's how you can use me:\n\n"
285
- "1. Set the toggle for Web Search and PDF Search from the checkbox in Additional Inputs drop down window\n"
286
- "2. Use web search to find information\n"
287
- "3. Upload the documents and ask questions about uploaded PDF documents by selecting your respective document\n"
288
- "4. For any queries feel free to reach out @[email protected] or discord - shreyas094\n\n"
289
- "To get started, upload some PDFs or ask me a question!")
290
- ]
291
 
292
- def refresh_documents():
293
- global uploaded_documents
294
- uploaded_documents = load_documents()
295
- return display_documents()
296
 
297
- # Define the checkbox outside the demo block
298
- document_selector = gr.CheckboxGroup(label="Select documents to query")
299
 
300
- use_web_search = gr.Checkbox(label="Use Web Search", value=True)
 
 
 
 
 
 
301
 
302
- custom_placeholder = "Ask a question (Note: You can toggle between Web Search and PDF Chat in Additional Inputs below)"
 
 
 
 
303
 
 
304
  css = """
305
- /* Fine-tune chatbox size */
306
- .chatbot-container {
307
- height: 600px !important;
308
- width: 100% !important;
309
- }
310
- .chatbot-container > div {
311
- height: 100%;
312
- width: 100%;
313
- }
314
  """
315
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
316
  demo = gr.ChatInterface(
317
- chatbot_interface,
318
  additional_inputs=[
319
- gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[3]),
320
  gr.Slider(minimum=0.1, maximum=1.0, value=0.2, step=0.1, label="Temperature"),
321
  gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of API Calls"),
322
- use_web_search,
323
- document_selector
324
  ],
325
  title="AI-powered Web Search and PDF Chat Assistant",
326
- description="Chat with your PDFs or use web search to answer questions. Toggle between Web Search and PDF Chat in Additional Inputs below.",
327
- theme=gr.themes.Soft(
328
- primary_hue="orange",
329
- secondary_hue="amber",
330
- neutral_hue="gray",
331
- font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]
332
- ).set(
333
- body_background_fill_dark="#0c0505",
334
- block_background_fill_dark="#0c0505",
335
- block_border_width="1px",
336
- block_title_background_fill_dark="#1b0f0f",
337
- input_background_fill_dark="#140b0b",
338
- button_secondary_background_fill_dark="#140b0b",
339
- border_color_accent_dark="#1b0f0f",
340
- border_color_primary_dark="#1b0f0f",
341
- background_fill_secondary_dark="#0c0505",
342
- color_accent_soft_dark="transparent",
343
- code_background_fill_dark="#140b0b"
344
- ),
345
  css=css,
346
  examples=[
347
- ["Tell me about the contents of the uploaded PDFs."],
348
- ["What are the main topics discussed in the documents?"],
349
- ["Can you summarize the key points from the PDFs?"]
350
  ],
351
  cache_examples=False,
352
  analytics_enabled=False,
@@ -356,50 +150,7 @@ demo = gr.ChatInterface(
356
  likeable=True,
357
  layout="bubble",
358
  height=400,
359
- value=initial_conversation()
360
  )
361
  )
362
 
363
- # Add file upload functionality
364
- with demo:
365
- gr.Markdown("## Upload and Manage PDF Documents")
366
-
367
- with gr.Row():
368
- file_input = gr.Files(label="Upload your PDF documents", file_types=[".pdf"])
369
- parser_dropdown = gr.Dropdown(choices=["pypdf", "llamaparse"], label="Select PDF Parser", value="llamaparse")
370
- update_button = gr.Button("Upload Document")
371
- refresh_button = gr.Button("Refresh Document List")
372
-
373
- update_output = gr.Textbox(label="Update Status")
374
- delete_button = gr.Button("Delete Selected Documents")
375
-
376
- # Update both the output text and the document selector
377
- update_button.click(update_vectors,
378
- inputs=[file_input, parser_dropdown],
379
- outputs=[update_output, document_selector])
380
-
381
- # Add the refresh button functionality
382
- refresh_button.click(refresh_documents,
383
- inputs=[],
384
- outputs=[document_selector])
385
-
386
- # Add the delete button functionality
387
- delete_button.click(delete_documents,
388
- inputs=[document_selector],
389
- outputs=[update_output, document_selector])
390
-
391
- gr.Markdown(
392
- """
393
- ## How to use
394
- 1. Upload PDF documents using the file input at the top.
395
- 2. Select the PDF parser (pypdf or llamaparse) and click "Upload Document" to update the vector store.
396
- 3. Select the documents you want to query using the checkboxes.
397
- 4. Ask questions in the chat interface.
398
- 5. Toggle "Use Web Search" to switch between PDF chat and web search.
399
- 6. Adjust Temperature and Number of API Calls to fine-tune the response generation.
400
- 7. Use the provided examples or ask your own questions.
401
- """
402
- )
403
-
404
- if __name__ == "__main__":
405
- demo.launch(share=True)
 
1
+ import gradio as gr
2
+ from duckduckgo_search import DDGS
3
+ from typing import List, Dict
4
  import os
 
5
  import logging
 
 
 
 
 
 
 
 
 
 
6
 
7
+ logging.basicConfig(level=logging.INFO)
 
8
 
9
  # Environment variables and configurations
10
  huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11
 
12
+ class ConversationManager:
13
+ def __init__(self):
14
+ self.history = []
15
+ self.current_context = None
 
16
 
17
+ def add_interaction(self, query, response):
18
+ self.history.append((query, response))
19
+ self.current_context = f"Previous query: {query}\nPrevious response summary: {response[:200]}..."
20
 
21
+ def get_context(self):
22
+ return self.current_context
23
 
24
+ def get_web_search_results(query: str, max_results: int = 10) -> List[Dict[str, str]]:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
25
  try:
26
+ results = list(DDGS().text(query, max_results=max_results))
27
+ if not results:
28
+ print(f"No results found for query: {query}")
29
+ return results
 
 
 
 
 
 
30
  except Exception as e:
31
+ print(f"An error occurred during web search: {str(e)}")
32
+ return [{"error": f"An error occurred during web search: {str(e)}"}]
33
+
34
+ def rephrase_query(original_query: str, conversation_manager: ConversationManager, model: str) -> str:
35
+ context = conversation_manager.get_context()
36
+ if context:
37
+ prompt = f"""You are a highly intelligent conversational chatbot. Your task is to analyze the given context and new query, then decide whether to rephrase the query with or without incorporating the context. Follow these steps:
38
+
39
+ 1. Determine if the new query is a continuation of the previous conversation or an entirely new topic.
40
+ 2. If it's a continuation, rephrase the query by incorporating relevant information from the context to make it more specific and contextual.
41
+ 3. If it's a new topic, rephrase the query to make it more appropriate for a web search, focusing on clarity and accuracy without using the previous context.
42
+ 4. Provide ONLY the rephrased query without any additional explanation or reasoning.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
43
 
44
+ Context: {context}
45
 
46
+ New query: {original_query}
 
 
 
 
 
 
 
 
 
 
 
 
47
 
48
+ Rephrased query:"""
49
+ response = DDGS().chat(prompt, model=model)
50
+ # Extract only the rephrased query, removing any explanations
51
+ rephrased_query = response.split('\n')[0].strip()
52
+ return rephrased_query
53
+ return original_query
54
+
55
+ def summarize_results(query: str, search_results: List[Dict[str, str]], conversation_manager: ConversationManager, model: str) -> str:
56
+ try:
57
+ context = conversation_manager.get_context()
58
+ search_context = "\n\n".join([f"Title: {result['title']}\nContent: {result['body']}" for result in search_results])
59
 
60
+ prompt = f"""You are a highly intelligent & expert analyst and your job is to skillfully articulate the web search results about '{query}' and considering the context: {context},
61
+ You have to create a comprehensive news summary FOCUSING on the context provided to you.
62
+ Include key facts, relevant statistics, and expert opinions if available.
63
+ Ensure the article is well-structured with an introduction, main body, and conclusion, IF NECESSARY.
64
+ Address the query in the context of the ongoing conversation IF APPLICABLE.
65
+ Cite sources directly within the generated text and not at the end of the generated text, integrating URLs where appropriate to support the information provided:
66
 
67
+ {search_context}
68
 
69
+ Article:"""
 
 
 
 
 
 
 
 
 
 
 
 
 
 
70
 
71
+ summary = DDGS().chat(prompt, model=model)
72
+ return summary
73
+ except Exception as e:
74
+ return f"An error occurred during summarization: {str(e)}"
 
 
 
 
75
 
76
+ conversation_manager = ConversationManager()
 
 
 
 
 
77
 
78
+ def respond(message, chat_history, temperature, num_api_calls, model):
79
+ final_summary = ""
80
+ original_query = message
81
+ rephrased_query = rephrase_query(message, conversation_manager, model)
 
 
 
 
 
82
 
83
+ logging.info(f"Original query: {original_query}")
84
+ logging.info(f"Rephrased query: {rephrased_query}")
 
 
85
 
86
+ for _ in range(num_api_calls):
87
+ search_results = get_web_search_results(rephrased_query)
88
 
89
+ if not search_results:
90
+ final_summary += f"No search results found for the query: {rephrased_query}\n\n"
91
+ elif "error" in search_results[0]:
92
+ final_summary += search_results[0]["error"] + "\n\n"
93
+ else:
94
+ summary = summarize_results(rephrased_query, search_results, conversation_manager, model)
95
+ final_summary += summary + "\n\n"
96
 
97
+ if final_summary:
98
+ conversation_manager.add_interaction(original_query, final_summary)
99
+ return final_summary
100
+ else:
101
+ return "Unable to generate a response. Please try a different query."
102
 
103
+ # The rest of your code (CSS, theme, and Gradio interface setup) remains the same
104
  css = """
105
+ Your custom CSS here
 
 
 
 
 
 
 
 
106
  """
107
 
108
+ custom_placeholder = "Ask me anything about web content"
109
+
110
+ theme = gr.themes.Soft(
111
+ primary_hue="orange",
112
+ secondary_hue="amber",
113
+ neutral_hue="gray",
114
+ font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]
115
+ ).set(
116
+ body_background_fill_dark="#0c0505",
117
+ block_background_fill_dark="#0c0505",
118
+ block_border_width="1px",
119
+ block_title_background_fill_dark="#1b0f0f",
120
+ input_background_fill_dark="#140b0b",
121
+ button_secondary_background_fill_dark="#140b0b",
122
+ border_color_accent_dark="#1b0f0f",
123
+ border_color_primary_dark="#1b0f0f",
124
+ background_fill_secondary_dark="#0c0505",
125
+ color_accent_soft_dark="transparent",
126
+ code_background_fill_dark="#140b0b"
127
+ )
128
+
129
  demo = gr.ChatInterface(
130
+ respond,
131
  additional_inputs=[
 
132
  gr.Slider(minimum=0.1, maximum=1.0, value=0.2, step=0.1, label="Temperature"),
133
  gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of API Calls"),
134
+ gr.Dropdown(["gpt-4o-mini", "claude-3-haiku", "llama-3.1-70b", "mixtral-8x7b"], label="Model")
 
135
  ],
136
  title="AI-powered Web Search and PDF Chat Assistant",
137
+ description="This AI-powered Web Search and PDF Chat Assistant combines real-time web search capabilities with advanced language processing.",
138
+ theme=theme,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
139
  css=css,
140
  examples=[
141
+ ["What is AI"],
142
+ ["Any recent news on US Banks"],
143
+ ["Who is Donald Trump"]
144
  ],
145
  cache_examples=False,
146
  analytics_enabled=False,
 
150
  likeable=True,
151
  layout="bubble",
152
  height=400,
 
153
  )
154
  )
155
 
156
+ demo.launch()