Update app.py

app.py

@@ -7,36 +7,9 @@ import openai
 # 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 Gemini API (replace with your actual API key)
+# Set up the openai API key (replace with your actual OpenAI API key)
 openai.api_key = 'your_openai.api_key_here'
 
-# Function to get solar radiation and tariff based on user input location
-def get_state_data(state_name):
-    state_data = df[df['State'].str.contains(state_name, case=False)]
-    if state_data.empty:
-        return None
-    return state_data.iloc[0]
-
-# Function to estimate solar system size (in kW)
-def estimate_system_size(roof_size, ghi):
-    # Simple formula: 1 kW system needs about 10-12 sq meters of roof space
-    system_size = roof_size / 10  # in kW (assuming 10 sq meters = 1 kW)
-    estimated_output = ghi * system_size  # kWh per day
-    return system_size, estimated_output
-
-# Function to calculate cost, savings, and payback period
-def estimate_cost_and_savings(system_size, state_data, electricity_bill):
-    # Cost per kW (₹)
-    solar_cost_per_kw = state_data['Solar_Cost_per_kW (₹)']
-    # Total solar system cost
-    system_cost = system_size * solar_cost_per_kw
-    # Savings per month assuming 100% replacement of electricity bill
-    savings_per_month = electricity_bill
-    # Payback period in months
-    payback_period = system_cost / (savings_per_month * 12)
-    
-    return system_cost, savings_per_month, payback_period
-
 # Streamlit UI
 st.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered")
 st.title("AI-based Solar Project Estimation Tool")
@@ -57,35 +30,46 @@ with st.form("solar_form"):
 
 if submitted and location and roof_size > 0 and electricity_bill >= 0:
     # Fetch state data from the dataset
-    state_data = get_state_data(location)
+    state_data = df[df['State'].str.contains(location, case=False)].iloc[0]  # Get the first match
+    
     if state_data is not None:
         ghi = state_data['Avg_GHI (kWh/m²/day)']
+        solar_cost_per_kw = state_data['Solar_Cost_per_kW (₹)']
         
-        # Estimate solar system size and output
-        system_size, estimated_output = estimate_system_size(roof_size, ghi)
+        # Use LLM 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
+        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}
         
-        # Estimate cost, savings, and payback period
-        system_cost, savings_per_month, payback_period = estimate_cost_and_savings(system_size, state_data, electricity_bill)
+        Provide:
+        1. Estimated solar system size in kW
+        2. Estimated daily solar output in kWh
+        3. Total system cost in ₹
+        4. Monthly savings in ₹
+        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,
+        )
         
-        # Display results
-        st.subheader("Estimated Solar System Details:")
-        st.write(f"**Location**: {location}")
-        st.write(f"**System Size**: {system_size:.2f} kW")
-        st.write(f"**Estimated Daily Output**: {estimated_output:.2f} kWh/day")
-        st.write(f"**Total System Cost**: ₹{system_cost:,.2f}")
-        st.write(f"**Monthly Savings**: ₹{savings_per_month:,.2f}")
-        st.write(f"**Payback Period**: {payback_period:.2f} years")
+        # Extract the result
+        result = response.choices[0].text.strip()
         
-        # Plot savings and payback period
-        fig, ax = plt.subplots()
-        ax.bar(['Total System Cost', 'Monthly Savings', 'Payback Period (months)'], 
-               [system_cost, savings_per_month, payback_period*12], color=['blue', 'green', 'orange'])
-        ax.set_ylabel("Amount (₹) / Time (Months)")
-        ax.set_title("Solar System Estimation Breakdown")
-        st.pyplot(fig)
+        # Display the response from the model
+        st.subheader("Estimated Solar System Details:")
+        st.write(result)
         
     else:
         st.error("Sorry, the location entered does not match any available data.")
 else:
     st.warning("Please fill out all fields to see your solar project estimate.")
-