Update app.py

app.py

@@ -1,51 +1,68 @@
+
 import streamlit as st
-import pandas as pd
-
-# Streamlit app title and description
-st.title("AI-Powered Inventory Management System")
-st.write("""
-This proof of concept demonstrates how IBM Watson and IBM Granite can be used to optimize retail inventory management.
-Upload synthetic data to get AI-driven insights.
-""")
-
-# Step 1: Upload synthetic data files
-st.header("Upload Your Inventory Data")
-uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
-
-if uploaded_file is not None:
-    # Step 2: Preview the uploaded data
-    data = pd.read_csv(uploaded_file)
-    st.subheader("Preview of the uploaded data:")
-    st.dataframe(data.head())
-
-    # Step 3: Call Watson and Granite APIs for AI processing (Placeholder functions)
-    if st.button("Generate AI Insights"):
-        st.subheader("AI-Powered Insights")
-
-        # Placeholder for Watson API call
-        demand_forecast = call_watson_api(data)
-        st.write("Demand Forecasting:")
-        st.write(demand_forecast)
-
-        # Placeholder for Granite API call
-        insights = call_granite_api(data)
-        st.write("AI-Generated Recommendations:")
-        st.write(insights)
-
-# Placeholder function for Watson API integration
-def call_watson_api(data):
-    # Simulated AI output
-    demand_forecast = {
-        "Product A": "Reorder in 5 days",
-        "Product B": "Stock sufficient for 10 days",
-    }
-    return demand_forecast
-
-# Placeholder function for Granite API integration
-def call_granite_api(data):
-    # Simulated AI output
-    insights = {
-        "Recommendation": "Run a promotion for Product C to clear overstock",
-        "Reorder Alert": "Product D needs restocking in 3 days",
-    }
-    return insights
+from PIL import Image
+import datetime
+import time
+from ibm_watsonx_ai import APIClient
+from ibm_watsonx_ai import Credentials
+import os
+from ibm_watsonx_ai.foundation_models.utils.enums import ModelTypes
+from ibm_watsonx_ai.foundation_models import ModelInference
+from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
+from ibm_watsonx_ai.foundation_models.utils.enums import DecodingMethods
+import requests
+import json
+
+st.title("August 2024 IBM Hackathon")
+st.header("Sinple app to find synonyms")
+
+st.sidebar.header("About")
+st.sidebar.text("Developed by Tony Pearson")
+
+project_id = os.environ['AUG24_PROJID']
+
+credentials = Credentials(
+    url = "https://us-south.ml.cloud.ibm.com",
+    api_key = os.environ['AUG24_APIKEY']
+)
+
+client = APIClient(credentials)
+client.set.default_project(project_id)
+
+parameters = {
+    GenParams.DECODING_METHOD: DecodingMethods.GREEDY,
+    GenParams.MIN_NEW_TOKENS: 1,
+    GenParams.MAX_NEW_TOKENS: 50,
+    GenParams.STOP_SEQUENCES: ["\n"]
+}
+
+model_id = ModelTypes.GRANITE_13B_CHAT_V2
+
+model = ModelInference(
+    model_id=model_id,
+    params=parameters,
+    credentials=credentials,
+    project_id = project_id
+    )
+    
+input_word = st.text_input("Enter your input word", "")
+    
+prompt_txt = """Generate a list of words similar to the input word."
+
+Input: wordy
+Output: verbose, loquacious, talkative
+
+Input: """
+
+prompt_input = prompt_txt + input_word + "\nOutput:"
+
+st.code(prompt_input)
+
+gen_parms_override = None
+
+# GENERATE
+   
+if st.button("Generate list of synonyms"):
+    generated_text_response = model.generate_text(prompt=prompt_input, params=parameters)
+    st.write("Output from generate_text() method:")
+    st.success(generated_text_response)