Update app.py

app.py

@@ -4,6 +4,12 @@ from langchain_google_genai import ChatGoogleGenerativeAI
 import os, spaces
 import pandas as pd
 
+import pandas as pd
+from google.cloud import aiplatform
+from google.cloud.aiplatform.gapic import PredictionServiceClient
+from google.cloud.aiplatform.gapic import ChatMessage
+
+
 os.environ['GOOGLE_API_KEY'] = os.getenv('geminiapi')
 
 # Function for LLM response
@@ -32,13 +38,55 @@ st.set_page_config(
 )
 
 st.header("File Insights🧊")
-uploaded_file = st.file_uploader("Upload csv file")
-if uploaded_file is not None:
-    df = pd.read_csv(uploaded_file, encoding = "ISO-8859-1")
-    st.write(df)
+# uploaded_file = st.file_uploader("Upload csv file")
+# if uploaded_file is not None:
+#     df = pd.read_csv(uploaded_file, encoding = "ISO-8859-1")
+#     st.write(df)
+
+# Initialize the AI platform client
+aiplatform.init(project="your-gcp-project-id", location="us-central1")
+
+# Example pandas DataFrame
+data = {
+    "Name": ["Alice", "Bob", "Charlie", "David"],
+    "Age": [25, 30, 35, 40],
+    "City": ["New York", "Los Angeles", "Chicago", "Houston"]
+}
+df = pd.DataFrame(data)
+
+# Define a function to interact with Google Gemini
+def chat_with_gemini(prompt: str, context: str = ""):
+    # Initialize the AI model and set it up for chat-like interactions
+    model = aiplatform.gapic.PredictionServiceClient()
+    
+    # Construct the message to send to Gemini
+    prompt_message = ChatMessage(
+        role="user", 
+        content=prompt
+    )
+    
+    context_message = ChatMessage(
+        role="system", 
+        content=context
+    )
+
+    # Send the request to Google Gemini and get the response
+    response = model.chat_messages(
+        model="projects/your-gcp-project-id/locations/us-central1/models/your-model-id",  # Model ID for Google Gemini
+        messages=[context_message, prompt_message]
+    )
+    
+    # Return the response content
+    return response[0].content
+
+# Convert the DataFrame to a text-based context for the model
+df_text = df.to_string(index=False)  # Converts DataFrame to a string for context
 
+# Example interaction: Ask the AI model a question about the DataFrame
+prompt = "What is the average age in the dataset?"
 
-# submit_btn = st.button("Submit")
+# Chat with Google Gemini AI using the DataFrame as context
+response = chat_with_gemini(prompt, context=df_text)
 
-# if submit_btn:
-#     llm_response(user_text, number_of_words) # function call for printing results
+# Print the AI's response
+print(response)