Create gemini_agent.py

#195
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  1. gemini_agent.py +660 -0
gemini_agent.py ADDED
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1
+ import os
2
+ import tempfile
3
+ import time
4
+ import re
5
+ import json
6
+ from typing import List, Optional, Dict, Any
7
+ from urllib.parse import urlparse
8
+ import requests
9
+ import yt_dlp
10
+ from bs4 import BeautifulSoup
11
+ from difflib import SequenceMatcher
12
+
13
+ from langchain_core.messages import HumanMessage, SystemMessage
14
+ from langchain_google_genai import ChatGoogleGenerativeAI
15
+ from langchain_community.utilities import DuckDuckGoSearchAPIWrapper, WikipediaAPIWrapper
16
+ from langchain.agents import Tool, AgentExecutor, ConversationalAgent, initialize_agent, AgentType
17
+ from langchain.memory import ConversationBufferMemory
18
+ from langchain.prompts import MessagesPlaceholder
19
+ from langchain.tools import BaseTool, Tool, tool
20
+ from google.generativeai.types import HarmCategory, HarmBlockThreshold
21
+ from PIL import Image
22
+ import google.generativeai as genai
23
+ from pydantic import Field
24
+
25
+ from smolagents import WikipediaSearchTool
26
+
27
+ class SmolagentToolWrapper(BaseTool):
28
+ """Wrapper for smolagents tools to make them compatible with LangChain."""
29
+
30
+ wrapped_tool: object = Field(description="The wrapped smolagents tool")
31
+
32
+ def __init__(self, tool):
33
+ """Initialize the wrapper with a smolagents tool."""
34
+ super().__init__(
35
+ name=tool.name,
36
+ description=tool.description,
37
+ return_direct=False,
38
+ wrapped_tool=tool
39
+ )
40
+
41
+ def _run(self, query: str) -> str:
42
+ """Use the wrapped tool to execute the query."""
43
+ try:
44
+ # For WikipediaSearchTool
45
+ if hasattr(self.wrapped_tool, 'search'):
46
+ return self.wrapped_tool.search(query)
47
+ # For DuckDuckGoSearchTool and others
48
+ return self.wrapped_tool(query)
49
+ except Exception as e:
50
+ return f"Error using tool: {str(e)}"
51
+
52
+ def _arun(self, query: str) -> str:
53
+ """Async version - just calls sync version since smolagents tools don't support async."""
54
+ return self._run(query)
55
+
56
+ class WebSearchTool:
57
+ def __init__(self):
58
+ self.last_request_time = 0
59
+ self.min_request_interval = 2.0 # Minimum time between requests in seconds
60
+ self.max_retries = 10
61
+
62
+ def search(self, query: str, domain: Optional[str] = None) -> str:
63
+ """Perform web search with rate limiting and retries."""
64
+ for attempt in range(self.max_retries):
65
+ # Implement rate limiting
66
+ current_time = time.time()
67
+ time_since_last = current_time - self.last_request_time
68
+ if time_since_last < self.min_request_interval:
69
+ time.sleep(self.min_request_interval - time_since_last)
70
+
71
+ try:
72
+ # Make the search request
73
+ results = self._do_search(query, domain)
74
+ self.last_request_time = time.time()
75
+ return results
76
+ except Exception as e:
77
+ if "202 Ratelimit" in str(e):
78
+ if attempt < self.max_retries - 1:
79
+ # Exponential backoff
80
+ wait_time = (2 ** attempt) * self.min_request_interval
81
+ time.sleep(wait_time)
82
+ continue
83
+ return f"Search failed after {self.max_retries} attempts: {str(e)}"
84
+
85
+ return "Search failed due to rate limiting"
86
+
87
+ def _do_search(self, query: str, domain: Optional[str] = None) -> str:
88
+ """Perform the actual search request."""
89
+ try:
90
+ # Construct search URL
91
+ base_url = "https://html.duckduckgo.com/html"
92
+ params = {"q": query}
93
+ if domain:
94
+ params["q"] += f" site:{domain}"
95
+
96
+ # Make request with increased timeout
97
+ response = requests.get(base_url, params=params, timeout=10)
98
+ response.raise_for_status()
99
+
100
+ if response.status_code == 202:
101
+ raise Exception("202 Ratelimit")
102
+
103
+ # Extract search results
104
+ results = []
105
+ soup = BeautifulSoup(response.text, 'html.parser')
106
+ for result in soup.find_all('div', {'class': 'result'}):
107
+ title = result.find('a', {'class': 'result__a'})
108
+ snippet = result.find('a', {'class': 'result__snippet'})
109
+ if title and snippet:
110
+ results.append({
111
+ 'title': title.get_text(),
112
+ 'snippet': snippet.get_text(),
113
+ 'url': title.get('href')
114
+ })
115
+
116
+ # Format results
117
+ formatted_results = []
118
+ for r in results[:10]: # Limit to top 5 results
119
+ formatted_results.append(f"[{r['title']}]({r['url']})\n{r['snippet']}\n")
120
+
121
+ return "## Search Results\n\n" + "\n".join(formatted_results)
122
+
123
+ except requests.RequestException as e:
124
+ raise Exception(f"Search request failed: {str(e)}")
125
+
126
+ def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
127
+ """
128
+ Save content to a temporary file and return the path.
129
+ Useful for processing files from the GAIA API.
130
+
131
+ Args:
132
+ content: The content to save to the file
133
+ filename: Optional filename, will generate a random name if not provided
134
+
135
+ Returns:
136
+ Path to the saved file
137
+ """
138
+ temp_dir = tempfile.gettempdir()
139
+ if filename is None:
140
+ temp_file = tempfile.NamedTemporaryFile(delete=False)
141
+ filepath = temp_file.name
142
+ else:
143
+ filepath = os.path.join(temp_dir, filename)
144
+
145
+ # Write content to the file
146
+ with open(filepath, 'w') as f:
147
+ f.write(content)
148
+
149
+ return f"File saved to {filepath}. You can read this file to process its contents."
150
+
151
+
152
+ def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
153
+ """
154
+ Download a file from a URL and save it to a temporary location.
155
+
156
+ Args:
157
+ url: The URL to download from
158
+ filename: Optional filename, will generate one based on URL if not provided
159
+
160
+ Returns:
161
+ Path to the downloaded file
162
+ """
163
+ try:
164
+ # Parse URL to get filename if not provided
165
+ if not filename:
166
+ path = urlparse(url).path
167
+ filename = os.path.basename(path)
168
+ if not filename:
169
+ # Generate a random name if we couldn't extract one
170
+ import uuid
171
+ filename = f"downloaded_{uuid.uuid4().hex[:8]}"
172
+
173
+ # Create temporary file
174
+ temp_dir = tempfile.gettempdir()
175
+ filepath = os.path.join(temp_dir, filename)
176
+
177
+ # Download the file
178
+ response = requests.get(url, stream=True)
179
+ response.raise_for_status()
180
+
181
+ # Save the file
182
+ with open(filepath, 'wb') as f:
183
+ for chunk in response.iter_content(chunk_size=8192):
184
+ f.write(chunk)
185
+
186
+ return f"File downloaded to {filepath}. You can now process this file."
187
+ except Exception as e:
188
+ return f"Error downloading file: {str(e)}"
189
+
190
+
191
+ def extract_text_from_image(image_path: str) -> str:
192
+ """
193
+ Extract text from an image using pytesseract (if available).
194
+
195
+ Args:
196
+ image_path: Path to the image file
197
+
198
+ Returns:
199
+ Extracted text or error message
200
+ """
201
+ try:
202
+ # Try to import pytesseract
203
+ import pytesseract
204
+ from PIL import Image
205
+
206
+ # Open the image
207
+ image = Image.open(image_path)
208
+
209
+ # Extract text
210
+ text = pytesseract.image_to_string(image)
211
+
212
+ return f"Extracted text from image:\n\n{text}"
213
+ except ImportError:
214
+ return "Error: pytesseract is not installed. Please install it with 'pip install pytesseract' and ensure Tesseract OCR is installed on your system."
215
+ except Exception as e:
216
+ return f"Error extracting text from image: {str(e)}"
217
+
218
+
219
+ def analyze_csv_file(file_path: str, query: str) -> str:
220
+ """
221
+ Analyze a CSV file using pandas and answer a question about it.
222
+
223
+ Args:
224
+ file_path: Path to the CSV file
225
+ query: Question about the data
226
+
227
+ Returns:
228
+ Analysis result or error message
229
+ """
230
+ try:
231
+ import pandas as pd
232
+
233
+ # Read the CSV file
234
+ df = pd.read_csv(file_path)
235
+
236
+ # Run various analyses based on the query
237
+ result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
238
+ result += f"Columns: {', '.join(df.columns)}\n\n"
239
+
240
+ # Add summary statistics
241
+ result += "Summary statistics:\n"
242
+ result += str(df.describe())
243
+
244
+ return result
245
+ except ImportError:
246
+ return "Error: pandas is not installed. Please install it with 'pip install pandas'."
247
+ except Exception as e:
248
+ return f"Error analyzing CSV file: {str(e)}"
249
+
250
+ @tool
251
+ def analyze_excel_file(file_path: str, query: str) -> str:
252
+ """
253
+ Analyze an Excel file using pandas and answer a question about it.
254
+
255
+ Args:
256
+ file_path: Path to the Excel file
257
+ query: Question about the data
258
+
259
+ Returns:
260
+ Analysis result or error message
261
+ """
262
+ try:
263
+ import pandas as pd
264
+
265
+ # Read the Excel file
266
+ df = pd.read_excel(file_path)
267
+
268
+ # Run various analyses based on the query
269
+ result = f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
270
+ result += f"Columns: {', '.join(df.columns)}\n\n"
271
+
272
+ # Add summary statistics
273
+ result += "Summary statistics:\n"
274
+ result += str(df.describe())
275
+
276
+ return result
277
+ except ImportError:
278
+ return "Error: pandas and openpyxl are not installed. Please install them with 'pip install pandas openpyxl'."
279
+ except Exception as e:
280
+ return f"Error analyzing Excel file: {str(e)}"
281
+
282
+ class GeminiAgent:
283
+ def __init__(self, api_key: str, model_name: str = "gemini-2.0-flash"):
284
+ # Suppress warnings
285
+ import warnings
286
+ warnings.filterwarnings("ignore", category=UserWarning)
287
+ warnings.filterwarnings("ignore", category=DeprecationWarning)
288
+ warnings.filterwarnings("ignore", message=".*will be deprecated.*")
289
+ warnings.filterwarnings("ignore", "LangChain.*")
290
+
291
+ self.api_key = api_key
292
+ self.model_name = model_name
293
+
294
+ # Configure Gemini
295
+ genai.configure(api_key=api_key)
296
+
297
+ # Initialize the LLM
298
+ self.llm = self._setup_llm()
299
+
300
+ # Setup tools
301
+ self.tools = [
302
+ SmolagentToolWrapper(WikipediaSearchTool()),
303
+ Tool(
304
+ name="analyze_video",
305
+ func=self._analyze_video,
306
+ description="Analyze YouTube video content directly"
307
+ ),
308
+ Tool(
309
+ name="analyze_image",
310
+ func=self._analyze_image,
311
+ description="Analyze image content"
312
+ ),
313
+ Tool(
314
+ name="analyze_table",
315
+ func=self._analyze_table,
316
+ description="Analyze table or matrix data"
317
+ ),
318
+ Tool(
319
+ name="analyze_list",
320
+ func=self._analyze_list,
321
+ description="Analyze and categorize list items"
322
+ ),
323
+ Tool(
324
+ name="web_search",
325
+ func=self._web_search,
326
+ description="Search the web for information"
327
+ )
328
+ ]
329
+
330
+ # Setup memory
331
+ self.memory = ConversationBufferMemory(
332
+ memory_key="chat_history",
333
+ return_messages=True
334
+ )
335
+
336
+ # Initialize agent
337
+ self.agent = self._setup_agent()
338
+
339
+
340
+ def run(self, query: str) -> str:
341
+ """Run the agent on a query with incremental retries."""
342
+ max_retries = 3
343
+ base_sleep = 1 # Start with 1 second sleep
344
+
345
+ for attempt in range(max_retries):
346
+ try:
347
+
348
+ # If no match found in answer bank, use the agent
349
+ response = self.agent.run(query)
350
+ return response
351
+
352
+ except Exception as e:
353
+ sleep_time = base_sleep * (attempt + 1) # Incremental sleep: 1s, 2s, 3s
354
+ if attempt < max_retries - 1:
355
+ print(f"Attempt {attempt + 1} failed. Retrying in {sleep_time} seconds...")
356
+ time.sleep(sleep_time)
357
+ continue
358
+ return f"Error processing query after {max_retries} attempts: {str(e)}"
359
+
360
+ print("Agent processed all queries!")
361
+
362
+ def _clean_response(self, response: str) -> str:
363
+ """Clean up the response from the agent."""
364
+ # Remove any tool invocation artifacts
365
+ cleaned = re.sub(r'> Entering new AgentExecutor chain...|> Finished chain.', '', response)
366
+ cleaned = re.sub(r'Thought:.*?Action:.*?Action Input:.*?Observation:.*?\n', '', cleaned, flags=re.DOTALL)
367
+ return cleaned.strip()
368
+
369
+ def run_interactive(self):
370
+ print("AI Assistant Ready! (Type 'exit' to quit)")
371
+
372
+ while True:
373
+ query = input("You: ").strip()
374
+ if query.lower() == 'exit':
375
+ print("Goodbye!")
376
+ break
377
+
378
+ print("Assistant:", self.run(query))
379
+
380
+ def _web_search(self, query: str, domain: Optional[str] = None) -> str:
381
+ """Perform web search with rate limiting and retries."""
382
+ try:
383
+ # Use DuckDuckGo API wrapper for more reliable results
384
+ search = DuckDuckGoSearchAPIWrapper(max_results=5)
385
+ results = search.run(f"{query} {f'site:{domain}' if domain else ''}")
386
+
387
+ if not results or results.strip() == "":
388
+ return "No search results found."
389
+
390
+ return results
391
+
392
+ except Exception as e:
393
+ return f"Search error: {str(e)}"
394
+
395
+ def _analyze_video(self, url: str) -> str:
396
+ """Analyze video content using Gemini's video understanding capabilities."""
397
+ try:
398
+ # Validate URL
399
+ parsed_url = urlparse(url)
400
+ if not all([parsed_url.scheme, parsed_url.netloc]):
401
+ return "Please provide a valid video URL with http:// or https:// prefix."
402
+
403
+ # Check if it's a YouTube URL
404
+ if 'youtube.com' not in url and 'youtu.be' not in url:
405
+ return "Only YouTube videos are supported at this time."
406
+
407
+ try:
408
+ # Configure yt-dlp with minimal extraction
409
+ ydl_opts = {
410
+ 'quiet': True,
411
+ 'no_warnings': True,
412
+ 'extract_flat': True,
413
+ 'no_playlist': True,
414
+ 'youtube_include_dash_manifest': False
415
+ }
416
+
417
+ with yt_dlp.YoutubeDL(ydl_opts) as ydl:
418
+ try:
419
+ # Try basic info extraction
420
+ info = ydl.extract_info(url, download=False, process=False)
421
+ if not info:
422
+ return "Could not extract video information."
423
+
424
+ title = info.get('title', 'Unknown')
425
+ description = info.get('description', '')
426
+
427
+ # Create a detailed prompt with available metadata
428
+ prompt = f"""Please analyze this YouTube video:
429
+ Title: {title}
430
+ URL: {url}
431
+ Description: {description}
432
+
433
+ Please provide a detailed analysis focusing on:
434
+ 1. Main topic and key points from the title and description
435
+ 2. Expected visual elements and scenes
436
+ 3. Overall message or purpose
437
+ 4. Target audience"""
438
+
439
+ # Use the LLM with proper message format
440
+ messages = [HumanMessage(content=prompt)]
441
+ response = self.llm.invoke(messages)
442
+ return response.content if hasattr(response, 'content') else str(response)
443
+
444
+ except Exception as e:
445
+ if 'Sign in to confirm' in str(e):
446
+ return "This video requires age verification or sign-in. Please provide a different video URL."
447
+ return f"Error accessing video: {str(e)}"
448
+
449
+ except Exception as e:
450
+ return f"Error extracting video info: {str(e)}"
451
+
452
+ except Exception as e:
453
+ return f"Error analyzing video: {str(e)}"
454
+
455
+ def _analyze_table(self, table_data: str) -> str:
456
+ """Analyze table or matrix data."""
457
+ try:
458
+ if not table_data or not isinstance(table_data, str):
459
+ return "Please provide valid table data for analysis."
460
+
461
+ prompt = f"""Please analyze this table:
462
+
463
+ {table_data}
464
+
465
+ Provide a detailed analysis including:
466
+ 1. Structure and format
467
+ 2. Key patterns or relationships
468
+ 3. Notable findings
469
+ 4. Any mathematical properties (if applicable)"""
470
+
471
+ messages = [HumanMessage(content=prompt)]
472
+ response = self.llm.invoke(messages)
473
+ return response.content if hasattr(response, 'content') else str(response)
474
+
475
+ except Exception as e:
476
+ return f"Error analyzing table: {str(e)}"
477
+
478
+ def _analyze_image(self, image_data: str) -> str:
479
+ """Analyze image content."""
480
+ try:
481
+ if not image_data or not isinstance(image_data, str):
482
+ return "Please provide a valid image for analysis."
483
+
484
+ prompt = f"""Please analyze this image:
485
+
486
+ {image_data}
487
+
488
+ Focus on:
489
+ 1. Visual elements and objects
490
+ 2. Colors and composition
491
+ 3. Text or numbers (if present)
492
+ 4. Overall context and meaning"""
493
+
494
+ messages = [HumanMessage(content=prompt)]
495
+ response = self.llm.invoke(messages)
496
+ return response.content if hasattr(response, 'content') else str(response)
497
+
498
+ except Exception as e:
499
+ return f"Error analyzing image: {str(e)}"
500
+
501
+ def _analyze_list(self, list_data: str) -> str:
502
+ """Analyze and categorize list items."""
503
+ if not list_data:
504
+ return "No list data provided."
505
+ try:
506
+ items = [x.strip() for x in list_data.split(',')]
507
+ if not items:
508
+ return "Please provide a comma-separated list of items."
509
+ # Add list analysis logic here
510
+ return "Please provide the list items for analysis."
511
+ except Exception as e:
512
+ return f"Error analyzing list: {str(e)}"
513
+
514
+ def _setup_llm(self):
515
+ """Set up the language model."""
516
+ # Set up model with video capabilities
517
+ generation_config = {
518
+ "temperature": 0.0,
519
+ "max_output_tokens": 2000,
520
+ "candidate_count": 1,
521
+ }
522
+
523
+ safety_settings = {
524
+ HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
525
+ HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
526
+ HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
527
+ HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
528
+ }
529
+
530
+ return ChatGoogleGenerativeAI(
531
+ model="gemini-2.0-flash",
532
+ google_api_key=self.api_key,
533
+ temperature=0,
534
+ max_output_tokens=2000,
535
+ generation_config=generation_config,
536
+ safety_settings=safety_settings,
537
+ system_message=SystemMessage(content=(
538
+ "You are a precise AI assistant that helps users find information and analyze content. "
539
+ "You can directly understand and analyze YouTube videos, images, and other content. "
540
+ "When analyzing videos, focus on relevant details like dialogue, text, and key visual elements. "
541
+ "For lists, tables, and structured data, ensure proper formatting and organization. "
542
+ "If you need additional context, clearly explain what is needed."
543
+ ))
544
+ )
545
+
546
+ def _setup_agent(self) -> AgentExecutor:
547
+ """Set up the agent with tools and system message."""
548
+
549
+ # Define the system message template
550
+ PREFIX = """You are a helpful AI assistant that can use various tools to answer questions and analyze content. You have access to tools for web search, Wikipedia lookup, and multimedia analysis.
551
+
552
+ TOOLS:
553
+ ------
554
+ You have access to the following tools:"""
555
+
556
+ FORMAT_INSTRUCTIONS = """To use a tool, use the following format:
557
+
558
+ Thought: Do I need to use a tool? Yes
559
+ Action: the action to take, should be one of [{tool_names}]
560
+ Action Input: the input to the action
561
+ Observation: the result of the action
562
+
563
+ When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
564
+
565
+ Thought: Do I need to use a tool? No
566
+ Final Answer: [your response here]
567
+
568
+ Begin! Remember to ALWAYS include 'Thought:', 'Action:', 'Action Input:', and 'Final Answer:' in your responses."""
569
+
570
+ SUFFIX = """Previous conversation history:
571
+ {chat_history}
572
+
573
+ New question: {input}
574
+ {agent_scratchpad}"""
575
+
576
+ # Create the base agent
577
+ agent = ConversationalAgent.from_llm_and_tools(
578
+ llm=self.llm,
579
+ tools=self.tools,
580
+ prefix=PREFIX,
581
+ format_instructions=FORMAT_INSTRUCTIONS,
582
+ suffix=SUFFIX,
583
+ input_variables=["input", "chat_history", "agent_scratchpad", "tool_names"],
584
+ handle_parsing_errors=True
585
+ )
586
+
587
+ # Initialize agent executor with custom output handling
588
+ return AgentExecutor.from_agent_and_tools(
589
+ agent=agent,
590
+ tools=self.tools,
591
+ memory=self.memory,
592
+ max_iterations=5,
593
+ verbose=True,
594
+ handle_parsing_errors=True,
595
+ return_only_outputs=True # This ensures we only get the final output
596
+ )
597
+
598
+ @tool
599
+ def analyze_csv_file(file_path: str, query: str) -> str:
600
+ """
601
+ Analyze a CSV file using pandas and answer a question about it.
602
+
603
+ Args:
604
+ file_path: Path to the CSV file
605
+ query: Question about the data
606
+
607
+ Returns:
608
+ Analysis result or error message
609
+ """
610
+ try:
611
+ import pandas as pd
612
+
613
+ # Read the CSV file
614
+ df = pd.read_csv(file_path)
615
+
616
+ # Run various analyses based on the query
617
+ result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
618
+ result += f"Columns: {', '.join(df.columns)}\n\n"
619
+
620
+ # Add summary statistics
621
+ result += "Summary statistics:\n"
622
+ result += str(df.describe())
623
+
624
+ return result
625
+ except ImportError:
626
+ return "Error: pandas is not installed. Please install it with 'pip install pandas'."
627
+ except Exception as e:
628
+ return f"Error analyzing CSV file: {str(e)}"
629
+
630
+ @tool
631
+ def analyze_excel_file(file_path: str, query: str) -> str:
632
+ """
633
+ Analyze an Excel file using pandas and answer a question about it.
634
+
635
+ Args:
636
+ file_path: Path to the Excel file
637
+ query: Question about the data
638
+
639
+ Returns:
640
+ Analysis result or error message
641
+ """
642
+ try:
643
+ import pandas as pd
644
+
645
+ # Read the Excel file
646
+ df = pd.read_excel(file_path)
647
+
648
+ # Run various analyses based on the query
649
+ result = f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
650
+ result += f"Columns: {', '.join(df.columns)}\n\n"
651
+
652
+ # Add summary statistics
653
+ result += "Summary statistics:\n"
654
+ result += str(df.describe())
655
+
656
+ return result
657
+ except ImportError:
658
+ return "Error: pandas and openpyxl are not installed. Please install them with 'pip install pandas openpyxl'."
659
+ except Exception as e:
660
+ return f"Error analyzing Excel file: {str(e)}"