Create app.py

app.py

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+# app.py
+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
+
+# Assuming your API key is set as an environment variable in Hugging Face Spaces secrets
+API_KEY = os.getenv("AIML_API_KEY")
+API_URL = "https://api.aimlapi.com/v1/chat/completions"
+
+def query_gpt5(user_input):
+    if not API_KEY:
+        return "API Key not set. Please set AIML_API_KEY environment variable."
+    headers = {"Authorization": f"Bearer {API_KEY}"}
+    data = {
+        "model": "openai/gpt-4o",
+        "messages": [
+            {"role": "system", "content": "You are an expert in battery materials and sustainable energy."},
+            {"role": "user", "content": user_input}
+        ]
+    }
+    try:
+        response = requests.post(API_URL, headers=headers, json=data)
+        response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)
+        resp_json = response.json()
+        if "choices" in resp_json:
+            return resp_json["choices"][0]["message"]["content"]
+        elif "error" in resp_json:
+             return f"Error from API: {resp_json['error']}"
+        else:
+            return f"Unexpected API response structure: {resp_json}"
+    except requests.exceptions.RequestException as e:
+        return f"API request failed: {e}"
+    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."
+)
+
+# 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.
+if __name__ == "__main__":
+    integrated_interface.launch()