Update app.py

app.py

@@ -1,14 +1,7 @@
-# app.py
+# app.py (Simplified for AI Chat Only)
 import gradio as gr
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
 import os
-import base64
-import requests # Assuming requests is used in query_gpt5
-
-# Ensure matplotlib uses an inline backend (though not strictly needed for saving files)
-# %matplotlib inline # This is a Colab magic command, not standard Python. Remove for app.py
+import requests
 
 # Assuming your API key is set as an environment variable in Hugging Face Spaces secrets
 API_KEY = os.getenv("AIML_API_KEY")
@@ -19,9 +12,9 @@ def query_gpt5(user_input):
         return "API Key not set. Please set AIML_API_KEY environment variable."
     headers = {"Authorization": f"Bearer {API_KEY}"}
     data = {
-        "model": "openai/gpt-4o",
+        "model": "openai/gpt-4o", # Or another model you have access to
         "messages": [
-            {"role": "system", "content": "You are an expert in battery materials and sustainable energy."},
+            {"role": "system", "content": "You are an expert assistant for battery materials and sustainable energy."},
             {"role": "user", "content": user_input}
         ]
     }
@@ -40,185 +33,16 @@ def query_gpt5(user_input):
     except Exception as e:
         return f"An error occurred during API query: {e}"
 
-
-# Define the analyze_eis function (modified to return file paths)
-def analyze_eis_and_get_text(eis_file):
-    if eis_file is None:
-        return "Please upload an EIS data file.", None, None
-
-    try:
-        # Load the uploaded file into a pandas DataFrame
-        # For deployment, Gradio provides a file-like object, access path via .name
-        df_eis = pd.read_csv(eis_file.name)
-
-        # Ensure required columns exist
-        if 'Frequency (Hz)' not in df_eis.columns or 'Z_real (ohm)' not in df_eis.columns or 'Z_imag (ohm)' not in df_eis.columns:
-            return "Uploaded file must contain 'Frequency (Hz)', 'Z_real (ohm)', and 'Z_imag (ohm)' columns.", None, None
-
-        # Calculate magnitude and phase
-        df_eis['Impedance Magnitude (ohm)'] = np.sqrt(df_eis['Z_real (ohm)']**2 + df_eis['Z_imag (ohm)']**2)
-        df_eis['Impedance Phase (deg)'] = np.arctan2(df_eis['Z_imag (ohm)'], df_eis['Z_real (ohm)']) * 180 / np.pi
-
-        # Generate Nyquist Plot
-        plt.figure(figsize=(6, 5))
-        plt.plot(df_eis['Z_real (ohm)'], -df_eis['Z_imag (ohm)'], marker='o', markersize=4, linestyle='-')
-        plt.xlabel('Z_real (ohm)')
-        plt.ylabel('-Z_imag (ohm)')
-        plt.title('Nyquist Plot')
-        plt.gca().set_aspect('equal', adjustable='box')
-        plt.grid(True)
-        nyquist_plot_path = "nyquist_plot.png"
-        plt.savefig(nyquist_plot_path)
-        plt.close() # Close the plot figure to free memory
-
-        # Generate Bode Plots
-        plt.figure(figsize=(10, 7))
-
-        # Magnitude Plot
-        plt.subplot(2, 1, 1)
-        plt.loglog(df_eis['Frequency (Hz)'], df_eis['Impedance Magnitude (ohm)'], marker='o', markersize=4, linestyle='-')
-        plt.xlabel('Frequency (Hz)')
-        plt.ylabel('Impedance Magnitude (ohm)')
-        plt.title('Bode Plot - Magnitude')
-        plt.grid(True, which="both", ls="--")
-
-        # Phase Plot
-        plt.subplot(2, 1, 2)
-        plt.semilogx(df_eis['Frequency (Hz)'], df_eis['Impedance Phase (deg)'], marker='o', markersize=4, linestyle='-')
-        plt.xlabel('Frequency (Hz)')
-        plt.ylabel('Phase (deg)')
-        plt.title('Bode Plot - Phase')
-        plt.grid(True, which="both", ls="--")
-
-        plt.tight_layout()
-        bode_plots_path = "bode_plots.png"
-        plt.savefig(bode_plots_path)
-        plt.close() # Close the plot figure
-
-        # Generate EIS analysis text
-        nyquist_description = "Nyquist plot analysis:\n"
-        # Handle potential empty dataframe or single point data
-        if not df_eis.empty:
-            rs_high_freq = df_eis['Z_real (ohm)'].iloc[-1]
-            nyquist_description += f"- High-frequency intercept (series resistance): {rs_high_freq:.2f} ohm.\n"
-
-            # Simple approximation for charge transfer resistance
-            if len(df_eis) > 1:
-                 mid_freq_real = df_eis['Z_real (ohm)'].iloc[len(df_eis)//2]
-                 rct_approx = mid_freq_real - rs_high_freq
-                 nyquist_description += f"- Semicircle observed in the mid-frequency range, indicating charge transfer processes. Approximate charge transfer resistance: {rct_approx:.2f} ohm.\n"
-            else:
-                nyquist_description += "- Not enough data points to approximate semicircle.\n"
-
-            # Describe low-frequency behavior (Warburg impedance)
-            if len(df_eis) > 1:
-                 low_freq_z = df_eis.iloc[:5] # Look at the first few low frequency points
-                 if len(low_freq_z) > 1:
-                    slopes = (low_freq_z['Z_imag (ohm)'].diff() / low_freq_z['Z_real (ohm)'].diff()).dropna()
-                    if any(abs(slope + 1) < 0.2 for slope in slopes): # Using a tolerance
-                        nyquist_description += "- Low-frequency tail shows a Warburg impedance behavior, characteristic of diffusion processes.\n"
-                    else:
-                         nyquist_description += "- Low-frequency behavior observed.\n"
-                 else:
-                     nyquist_description += "- Not enough low-frequency data points to determine Warburg behavior.\n"
-            else:
-                 nyquist_description += "- Not enough data points for low-frequency analysis.\n"
-
-
-            bode_mag_description = "Bode Magnitude plot analysis:\n"
-            bode_mag_description += f"- At high frequencies, the impedance magnitude is around {df_eis['Impedance Magnitude (ohm)'].iloc[-1]:.2f} ohm.\n"
-            bode_mag_description += f"- At low frequencies, the impedance magnitude increases significantly, reaching {df_eis['Impedance Magnitude (ohm)'].iloc[0]:.2f} ohm, indicating capacitive or diffusion-limited behavior.\n"
-            bode_mag_description += "- The magnitude plot shows a decrease with frequency in the mid-range, followed by an increase at low frequencies.\n"
-
-            bode_phase_description = "Bode Phase plot analysis:\n"
-            bode_phase_description += f"- At high frequencies, the phase angle is around {df_eis['Impedance Phase (deg)'].iloc[-1]:.2f} degrees, close to 0.\n"
-            if len(df_eis) > 1:
-                min_phase_idx = df_eis['Impedance Phase (deg)'].idxmin()
-                min_phase_freq = df_eis['Frequency (Hz)'].iloc[min_phase_idx]
-                min_phase_value = df_eis['Impedance Phase (deg)'].iloc[min_phase_idx]
-                bode_phase_description += f"- A phase minimum of {min_phase_value:.2f} degrees is observed around {min_phase_freq:.4f} Hz, corresponding to charge transfer processes.\n"
-            else:
-                 bode_phase_description += "- Not enough data points to identify phase minimum.\n"
-            bode_phase_description += f"- At low frequencies, the phase angle approaches {df_eis['Impedance Phase (deg)'].iloc[0]:.2f} degrees, consistent with capacitive or diffusion behavior.\n"
-        else:
-            nyquist_description += "- No data available for analysis.\n"
-            bode_mag_description = "Bode Magnitude plot analysis:\n- No data available for analysis.\n"
-            bode_phase_description = "Bode Phase plot analysis:\n- No data available for analysis.\n"
-
-
-        eis_analysis_text = "Electrochemical Impedance Spectroscopy (EIS) Analysis:\n\n" + \
-                            nyquist_description + "\n" + \
-                            bode_mag_description + "\n" + \
-                            bode_phase_description + "\n" + \
-                            "Note: Parameters like charge transfer resistance and Warburg impedance are estimated from visual features; precise values would require equivalent circuit fitting."
-
-        # Return the analysis text and paths to the saved plot files
-        return eis_analysis_text, nyquist_plot_path, bode_plots_path
-
-    except Exception as e:
-        # Clean up generated files if an error occurs
-        if os.path.exists("nyquist_plot.png"):
-            os.remove("nyquist_plot.png")
-        if os.path.exists("bode_plots.png"):
-            os.remove("bode_plots.png")
-        return f"Error processing EIS data: {e}", None, None
-
-
-# Modify the material_analysis function to accept EIS analysis text
-def material_analysis(query, eis_analysis_text):
-    combined_query = query
-    if eis_analysis_text and eis_analysis_text != "Please upload an EIS data file.":
-        combined_query = query + "\n\nEIS Analysis:\n" + eis_analysis_text
-
-    answer = query_gpt5(combined_query)
-    return answer
-
-
-# Define the integrated function that first analyzes EIS and then queries the AI
-def process_eis_and_query_ai(eis_file, ai_query):
-    # Analyze EIS data - this will save plot files and return analysis text
-    eis_analysis_text, nyquist_plot_path, bode_plots_path = analyze_eis_and_get_text(eis_file)
-
-    # Query AI with the analysis text
-    # The AI query should only proceed if there's a valid AI query input
-    ai_response_text = ""
-    if ai_query:
-        ai_response_text = material_analysis(ai_query, eis_analysis_text)
-
-    # Return all outputs
-    # Ensure paths are returned even if AI query is empty
-    return eis_analysis_text, nyquist_plot_path, bode_plots_path, ai_response_text
-
-
-# Define Gradio Interface components
-eis_file_input = gr.File(label="Upload EIS Data (CSV)")
-eis_analysis_output = gr.Textbox(label="EIS Analysis Summary", interactive=False)
-# Use Image component for plot files - Gradio will handle displaying the file from the path
-nyquist_plot_output = gr.Image(label="Nyquist Plot", type="filepath")
-bode_plots_output = gr.Image(label="Bode Plots", type="filepath")
-ai_query_input = gr.Textbox(label="Ask about Battery Materials or Recycling")
-ai_response_output = gr.Textbox(label="AI Research Assistant")
-
-
-# Create the integrated Gradio interface
-integrated_interface = gr.Interface(
-    fn=process_eis_and_query_ai,
-    inputs=[
-        eis_file_input,
-        ai_query_input
-    ],
-    outputs=[
-        eis_analysis_output,
-        nyquist_plot_output,
-        bode_plots_output,
-        ai_response_output
-    ],
-    title="🔋 VoltAIon - Integrated EIS Analysis and AI Chat",
-    description="Upload EIS data (CSV with Frequency, Z_real, Z_imag) and ask the AI about battery materials, informed by the analysis."
+# Define Gradio Interface
+ai_chat_interface = gr.Interface(
+    fn=query_gpt5,
+    inputs=gr.Textbox(label="Ask about Battery Materials or Sustainable Energy"),
+    outputs=gr.Textbox(label="AI Research Assistant"),
+    title="🤖 VoltAIon - AI Research Assistant (API Key Focus)",
+    description="Ask questions about battery materials and sustainable energy using the API."
 )
 
-# Launch the integrated interface
-# For deployment on Hugging Face Spaces, use launch(share=True) or simply launch()
-# Gradio handles the public URL for Spaces automatically.
+# Launch the interface
+# For deployment on Hugging Face Spaces, use launch()
 if __name__ == "__main__":
-    integrated_interface.launch()
+    ai_chat_interface.launch()