AI Blogen - WIP - 0.0.00000.001

lib/gpt_providers/gemini_arvix_image_details.py

@@ -0,0 +1,79 @@
+"""
+"""
+import os
+import logging
+from pathlib import Path
+
+import google.generativeai as genai
+logging.basicConfig(level=logging.INFO, format='%(asctime)s-%(levelname)s-%(module)s-%(lineno)d-%(message)s')
+from dotenv import load_dotenv
+load_dotenv(Path('../../.env'))
+
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_random_exponential,
+) # for exponential backoff
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+def gemini_arxiv_img_info(img_path):
+    """ Get image details from arxiv papers. """
+    try:
+        genai.configure(api_key=os.getenv("API_KEY"))
+    except Exception as e:
+        logging.error(f"Could not load gemini API key: {e}")
+        raise e
+
+    # Set up the model
+    generation_config = {
+        "temperature": 0.9,
+        "top_p": 1,
+        "top_k": 1,
+        "max_output_tokens": 1096,
+    }
+
+    safety_settings = [{
+        "category": "HARM_CATEGORY_HARASSMENT",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+    },
+    {
+        "category": "HARM_CATEGORY_HATE_SPEECH",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+    },
+    {
+        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+    },
+    {
+        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+    },]
+
+    try:
+        model = genai.GenerativeModel(model_name="gemini-pro-vision",
+            generation_config=generation_config,
+            safety_settings=safety_settings)
+    except Exception as e:
+        logging.error(f"Could not create GenerativeModel: {e}")
+        raise e
+
+    # Validate that an image is present
+    if not (img := Path(img_path)).exists():
+        raise FileNotFoundError(f"Could not find image: {img}")
+
+    image_parts = [{
+        "mime_type": "image/png",
+        "data": Path(img_path).read_bytes()
+    },]
+
+    prompt_parts = [
+        "As scholar on evaluating research papers, I will provide you with an image from a research paper. Your task is to explain the image in details so that I can use it in a blog article. Explain the key findings and conclusions from the image. Your description should be in simple terms to explain to a wider audience. Explain key findings from the given image.",
+        image_parts[0],]
+
+    try:
+        response = model.generate_content(prompt_parts)
+        return response.text
+    except Exception as e:
+        logging.error(f"Could not generate gemini content: {e}")
+        raise e

lib/gpt_providers/gemini_pro_text.py

@@ -7,6 +7,7 @@ import google.generativeai as genai
 logging.basicConfig(level=logging.INFO, format='%(asctime)s-%(levelname)s-%(module)s-%(lineno)d-%(message)s')
 from dotenv import load_dotenv
 load_dotenv(Path('../../.env'))
+from .mistral_chat_completion import mistral_text_response
 
 from tenacity import (
     retry,
@@ -17,7 +18,7 @@ from tenacity import (
 
 @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
 def gemini_text_response(prompt):
-    """ Provide a programming blog and get code exmaples."""
+    """ Common functiont to get response from gemini pro Text. """
     genai.configure(api_key=os.getenv('GEMINI_API_KEY'))
 
     # Set up the model
@@ -25,14 +26,17 @@ def gemini_text_response(prompt):
         "temperature": 1,
         "top_p": 1,
         "top_k": 1,
-        "max_output_tokens": 4096,
+        "max_output_tokens": 6096,
     }
 
     model = genai.GenerativeModel(model_name="gemini-pro", generation_config=generation_config)
     try:
         response = model.generate_content(prompt)
-    except Exception as err:
-        logger.error(f"Failed to get response from Gemini: {err}. Retrying..")
-        gemini_research_report(query)
 
+    except Exception as err:
+        logger.error(f"Failed to get response from Gemini: {err}. Retrying.")
+        # Try with minstral.
+        print(f"\n\n\n--MINSTRAL--\n\n\n\n")
+        response = mistral_text_response(prompt)
+        return response
     return response.text

lib/gpt_providers/mistral_chat_completion.py

@@ -0,0 +1,40 @@
+import os
+import logging
+from pathlib import Path
+
+from mistralai.client import MistralClient
+from mistralai.models.chat_completion import ChatMessage
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s-%(levelname)s-%(module)s-%(lineno)d-%(message)s')
+from dotenv import load_dotenv
+load_dotenv(Path('../../.env'))
+
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_random_exponential,
+)  # for exponential backoff
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+def mistral_text_response(prompt):
+    """ Common function to get text response from minstral. """
+    api_key = os.environ["MISTRAL_API_KEY"]
+    model = "mistral-medium"
+
+    client = MistralClient(api_key=api_key)
+
+    messages = [
+        ChatMessage(role="user", content=prompt)
+    ]
+
+    # No streaming
+    chat_response = client.chat(
+        model=model,
+        messages=messages,
+    )
+    print(chat_response)
+
+    # With streaming
+    for chunk in client.chat_stream(model=model, messages=messages):
+        print(chunk)