Update app.py

app.py

@@ -4,11 +4,18 @@ import numpy as np
 import matplotlib.pyplot as plt
 import openai
 
+import streamlit as st
+import pandas as pd
+import requests
+
 # Load the solar data CSV file
 df = pd.read_csv('https://huggingface.co/spaces/MLDeveloper/AI_based_Solar_Project_Estimation_Tool/resolve/main/solar_data_india_2024.csv')
 
-# Set up the openai API key (replace with your actual OpenAI API key)
-openai.api_key = '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'
+# Set up the Gemini API key (replace with your actual Gemini API key)
+GEMINI_API_KEY = 'AIzaSyAGGP8I7c0YmA8xKZsEdAF9AF3ElaPoEn4'
+
+# Set the API endpoint for Gemini
+GEMINI_API_URL = "https://gemini.googleapis.com/v1/completions"  # Example URL, please check the actual URL
 
 # Streamlit UI
 st.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered")
@@ -36,7 +43,7 @@ if submitted and location and roof_size > 0 and electricity_bill >= 0:
         ghi = state_data['Avg_GHI (kWh/m²/day)']
         solar_cost_per_kw = state_data['Solar_Cost_per_kW (₹)']
         
-        # Use LLM to generate solar project estimate (cost, savings, payback period)
+        # Use Gemini API to generate solar project estimate (cost, savings, payback period)
         prompt = f"""
         Estimate the solar system for the location '{location}' based on the following details:
         Roof size: {roof_size} sq meters
@@ -52,22 +59,31 @@ if submitted and location and roof_size > 0 and electricity_bill >= 0:
         5. Payback period in years
         """
 
-        # Get response from OpenAI API
-        response = openai.Completion.create(
-            engine="gpt-4",  # You can change this to another GPT model if needed
-            prompt=prompt,
-            max_tokens=250,
-            n=1,
-            stop=None,
-            temperature=0.7,
+        # Request to Gemini API (Adjust headers and body according to Gemini API documentation)
+        response = requests.post(
+            GEMINI_API_URL,
+            headers={
+                "Authorization": f"Bearer {GEMINI_API_KEY}",
+                "Content-Type": "application/json"
+            },
+            json={
+                "model": "gemini-model-xyz",  # Use the actual Gemini model name
+                "prompt": prompt,
+                "max_tokens": 250,
+                "temperature": 0.7,
+                "top_p": 1.0,
+                "n": 1
+            }
         )
         
-        # Extract the result
-        result = response.choices[0].text.strip()
-        
-        # Display the response from the model
-        st.subheader("Estimated Solar System Details:")
-        st.write(result)
+        # Check for successful response
+        if response.status_code == 200:
+            result = response.json().get("choices", [])[0].get("text", "").strip()
+            # Display the response from the Gemini model
+            st.subheader("Estimated Solar System Details:")
+            st.write(result)
+        else:
+            st.error(f"Error: {response.status_code}. Unable to get response from Gemini API.")
         
     else:
         st.error("Sorry, the location entered does not match any available data.")