Update app.py

app.py

@@ -2,33 +2,31 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-import openai
-
-import streamlit as st
-import pandas as pd
+import os
 import requests
+from dotenv import load_dotenv
 
-# 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')
+# Load environment variables from .env file
+load_dotenv()
+
+# Get the Gemini API key from the environment
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
 
-# Set up the Gemini API key (replace with your actual Gemini API key)
-GEMINI_API_KEY = 'AIzaSyAGGP8I7c0YmA8xKZsEdAF9AF3ElaPoEn4'
+# Gemini API URL
+GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent"
 
-# Set the API endpoint for Gemini
-GEMINI_API_URL = "https://gemini.googleapis.com/v1/completions"  # Example URL, please check the actual URL
+# 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')
 
 # Streamlit UI
 st.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered")
 st.title("AI-based Solar Project Estimation Tool")
 
-# Center all input widgets
 st.write("### Enter Your Details Below:")
 
 with st.form("solar_form"):
-    # Get the list of unique states from the dataset
     state_options = df['State'].dropna().unique()
     
-    # Input widgets
     location = st.selectbox("Select your State", options=sorted(state_options))
     roof_size = st.number_input("Enter your roof size (in sq meters)", min_value=1)
     electricity_bill = st.number_input("Enter your monthly electricity bill (₹)", min_value=0)
@@ -36,21 +34,19 @@ with st.form("solar_form"):
     submitted = st.form_submit_button("Get Estimate")
 
 if submitted and location and roof_size > 0 and electricity_bill >= 0:
-    # Fetch state data from the dataset
-    state_data = df[df['State'].str.contains(location, case=False)].iloc[0]  # Get the first match
+    state_data = df[df['State'].str.contains(location, case=False)].iloc[0]
     
     if state_data is not None:
         ghi = state_data['Avg_GHI (kWh/m²/day)']
         solar_cost_per_kw = state_data['Solar_Cost_per_kW (₹)']
         
-        # Use Gemini API to generate solar project estimate (cost, savings, payback period)
-        prompt = f"""
+        prompt_text = f"""
         Estimate the solar system for the location '{location}' based on the following details:
-        Roof size: {roof_size} sq meters
-        Monthly electricity bill: ₹{electricity_bill}
-        Average GHI (solar radiation) for {location}: {ghi} kWh/m²/day
-        Solar system cost per kW in {location}: ₹{solar_cost_per_kw}
-        
+        - Roof size: {roof_size} sq meters
+        - Monthly electricity bill: ₹{electricity_bill}
+        - Average GHI (solar radiation) for {location}: {ghi} kWh/m²/day
+        - Solar system cost per kW in {location}: ₹{solar_cost_per_kw}
+
         Provide:
         1. Estimated solar system size in kW
         2. Estimated daily solar output in kWh
@@ -59,31 +55,33 @@ if submitted and location and roof_size > 0 and electricity_bill >= 0:
         5. Payback period in years
         """
 
-        # 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
-            }
-        )
-        
-        # Check for successful response
+        # Define the headers for the API request
+        headers = {
+            "Authorization": f"Bearer {GEMINI_API_KEY}",
+            "Content-Type": "application/json"
+        }
+
+        # Define the request payload
+        payload = {
+            "model": "gemini-1.5-flash",
+            "messages": [{"role": "system", "content": "You are a helpful assistant."},
+                         {"role": "user", "content": prompt_text}],
+            "temperature": 0.7
+        }
+
+        # Make the API request
+        response = requests.post(GEMINI_API_URL, json=payload, headers=headers)
+
+        # Check the response from the API
         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)
+            result = response.json()
+            generated_content = result['choices'][0]['message']['content']
+            
+            # Display the generated content (solar estimates)
+            st.subheader("Solar Project Estimate")
+            st.write(generated_content)
         else:
-            st.error(f"Error: {response.status_code}. Unable to get response from Gemini API.")
+            st.error(f"Error: {response.status_code} - {response.text}")
         
     else:
         st.error("Sorry, the location entered does not match any available data.")