gemini added

app.py

@@ -6,10 +6,16 @@ from groq import Groq
 from logic import LLMClient, CodeProcessor
 from batch_code_logic_csv import csv_read_batch_code
 import zipfile
+from dotenv import load_dotenv
 import io
+import google.generativeai as genai
+import re
+
 
 client = Groq(api_key=os.getenv("GROQ_API_KEY"))
-llm_obj = LLMClient(client)
+
+GOOGLE_API_KEY=os.getenv('GOOGLE_API_KEY')
+llm_obj = LLMClient(client,GOOGLE_API_KEY)
 processor = CodeProcessor(llm_obj)
 
 st.title("Code Analysis with LLMs")
@@ -32,10 +38,17 @@ elif code_input_method == "Upload Code File":
     uploaded_file = st.sidebar.file_uploader("Upload your code file", type=["py", "txt"])
     if uploaded_file is not None:
         code_text = uploaded_file.read().decode("utf-8")
-        code_dict = {"single_code": code_text}
+
+        code_lines = [line.strip() for line in code_text.splitlines()]
+        code_lines = [re.sub(r"\s+", " ", line) for line in code_lines]
+
+        # Join the cleaned lines back if needed as a single string
+        clean_code_text = "\n".join(code_lines)  # Combine lines into one string
+
+        code_dict = {"single_code": clean_code_text}
 
 model_choice = st.sidebar.selectbox("Select LLM Model",
-                                    ["llama-3.2-90b-text-preview", "llama-3.2-90b-text-preview", "llama3-8b-8192"])
+                                    ["llama-3.2-90b-text-preview", "llama-3.2-90b-text-preview", "llama3-8b-8192","llama-3.1-70b-versatile","gemma2-9b-it","gemini-pro"])
 
 if code_dict:
     unique_key = st.sidebar.selectbox("Select a Key for Analysis", list(code_dict.keys()))

logic.py

@@ -1,15 +1,23 @@
+import google.generativeai as genai
 class LLMClient:
-    def __init__(self, api_client):
+    def __init__(self, api_client,google_api_key):
         self.client = api_client
+        self.google_api_key = google_api_key  # Google API key for Gemini
+        genai.configure(api_key=self.google_api_key)
 
     def chat_completion(self, messages, model="llama3-8b-8192"):
-        completion = self.client.chat.completions.create(
-            messages=messages,
-            model=model,
-            temperature=0.5,
-            stream=False,
-        )
-        return completion.choices[0].message.content
+        if model == "gemini-pro":
+                gemini_model = genai.GenerativeModel('gemini-pro')
+                response = gemini_model.generate_content([messages[1]['content']])
+                return response.text # Return the Gemini result
+        else:
+                completion = self.client.chat.completions.create(
+                    messages=messages,
+                    model=model,
+                    temperature=0.5,
+                    stream=False,
+                )
+                return completion.choices[0].message.content
 
 
 class CodeProcessor:

requirements.txt

@@ -1,4 +1,5 @@
 groq
 streamlit
 pandas
-numpy
+numpy
+google-generativeai