test for pr

CAG.py

@@ -6,6 +6,17 @@
 from colorama import Fore, Style, init
 import json
 import time
+import logging
+
+# ------------------ Logging Setup ------------------
+logging.basicConfig(
+    level=logging.INFO,  # Change to DEBUG if you want detailed logs
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    handlers=[
+        logging.FileHandler("extinct_animals.log"),  # log to file
+        logging.StreamHandler()  # also print to console
+    ]
+)
 
 # Initialize colorama
 init(autoreset=True)
@@ -15,41 +26,63 @@
 
 # Step 1: Extract Text from PDF
 def extract_text_from_pdf(pdf_path):
-    with open(pdf_path, 'rb') as file:
-        reader = PyPDF2.PdfReader(file)
-        return ''.join([page.extract_text() for page in reader.pages])
+    try:
+        logging.info(f"Extracting text from PDF: {pdf_path}")
+        with open(pdf_path, 'rb') as file:
+            reader = PyPDF2.PdfReader(file)
+            text = ''.join([page.extract_text() for page in reader.pages])
+        logging.info("PDF text extraction successful")
+        return text
+    except Exception as e:
+        logging.error(f"Failed to extract PDF: {e}")
+        return ""
 
 # Step 2: Initialize Knowledge Cache
 extinct_animals_pdf = "Extinct Animals.pdf"
 knowledge_cache = extract_text_from_pdf(extinct_animals_pdf)[:6000]  # Truncate to fit context limit
 
+if not knowledge_cache:
+    logging.warning("Knowledge cache is empty. Check the PDF file.")
+
 # Step 3: Set Up Groq API Client
-GROQ_API_KEY = os.environ["GROQ_API_KEY"] 
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+if not GROQ_API_KEY:
+    logging.error("GROQ_API_KEY not found in environment variables!")
+else:
+    logging.info("Groq API key loaded successfully")
 
 async def generate_response_async(prompt):
-    async with AsyncGroq(api_key=GROQ_API_KEY) as client:
-        chat_completion = await client.chat.completions.create(
-            messages=[{"role": "user", "content": prompt}],
-            model="llama-3.1-8b-instant",
-            max_tokens=1024,
-        )
-        return chat_completion.choices[0].message.content.strip()
+    try:
+        async with AsyncGroq(api_key=GROQ_API_KEY) as client:
+            logging.debug("Sending request to Groq API...")
+            chat_completion = await client.chat.completions.create(
+                messages=[{"role": "user", "content": prompt}],
+                model="llama-3.1-8b-instant",
+                max_tokens=1024,
+            )
+            response = chat_completion.choices[0].message.content.strip()
+            logging.info("Groq API response received")
+            return response
+    except Exception as e:
+        logging.error(f"Error generating response: {e}")
+        return "Sorry, I couldn't process your request."
 
 # Step 4: Implement CAG Logic
 async def extinct_animal_query(question: str) -> str:
+    logging.info(f"Processing question: {question}")
     prompt = f"""EXTINCT ANIMAL KNOWLEDGE BASE:
 {knowledge_cache}
 
 QUESTION: {question}
 ANSWER:"""
 
     response = await generate_response_async(prompt)
+    logging.info(f"Answer generated: {response[:100]}...")  # log first 100 chars
     return response
-
 # Step 5: Save Interaction History to JSON
 def save_to_history(question, answer):
     history_file = 'history.json'
-
+    # This is save history
     # Load existing history if it exists
     if os.path.exists(history_file):
         with open(history_file, 'r') as file:
@@ -92,3 +125,4 @@ def save_to_history(question, answer):
 
         except Exception as e:
             print(Fore.RED + f"An error occurred: {str(e)}")
+

app.py

@@ -11,6 +11,9 @@
 # Load environment variables
 load_dotenv()
 
+for i n range(10):
+    print(i)
+
 # --- Setup ---
 def extract_text_from_pdf(pdf_path):
     with open(pdf_path, 'rb') as file:
@@ -139,3 +142,4 @@ async def augmented_generation(question: str, knowledge_cache: str, groq_api_key
 
     # Add assistant response to chat history
     st.session_state["chat_history"].append({"role": "assistant", "content": full_response})
+