Update app.py

app.py

@@ -7,56 +7,75 @@ import requests
 
 # Groq API Setup
 API_KEY = "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"
-API_URL = "https://api.groq.com/openai/v1/chat/completions"  
+API_URL = "https://api.groq.com/openai/v1/chat/completions" 
 
 def analyze_file(uploaded_file):
-    # Load the file into a pandas DataFrame
-    if uploaded_file.name.endswith('.csv'):
-        df = pd.read_csv(uploaded_file.name)
-    elif uploaded_file.name.endswith('.xlsx'):
-        df = pd.read_excel(uploaded_file.name)
+    try:
+        # Load the file into a pandas DataFrame
+        if uploaded_file.name.endswith('.csv'):
+            df = pd.read_csv(uploaded_file.name)
+        elif uploaded_file.name.endswith('.xlsx'):
+            df = pd.read_excel(uploaded_file.name)
+        else:
+            return "Error: The uploaded file is neither CSV nor Excel."
+
+        # Display the column names and first few rows for debugging
+        print("Columns in the uploaded file:", df.columns)
+        print("Preview of the uploaded data:", df.head())
+
+        # Check if required columns are present
+        if 'Gain (dB)' in df.columns and 'Frequency (GHz)' in df.columns:
+            # Perform basic data analysis
+            mean_gain = df['Gain (dB)'].mean()
+            median_gain = df['Gain (dB)'].median()
+            std_dev_gain = df['Gain (dB)'].std()
+            
+            # Display analysis results
+            print(f"Mean Gain: {mean_gain}")
+            print(f"Median Gain: {median_gain}")
+            print(f"Standard Deviation of Gain: {std_dev_gain}")
+
+            # Plotting the data
+            fig, ax = plt.subplots()
+            ax.plot(df['Frequency (GHz)'], df['Gain (dB)'], label='Gain (dB)', color='blue')
+            ax.set_xlabel('Frequency (GHz)')
+            ax.set_ylabel('Gain (dB)')
+            ax.set_title('Gain vs Frequency')
+            ax.legend()
+            plt.close(fig)  # Close to avoid inline display
 
-    # Perform basic data analysis
-    if 'Gain (dB)' in df.columns and 'Frequency (GHz)' in df.columns:
-        mean_gain = df['Gain (dB)'].mean()
-        median_gain = df['Gain (dB)'].median()
-        std_dev_gain = df['Gain (dB)'].std()
-        
-        # Plot the data
-        fig, ax = plt.subplots()
-        ax.plot(df['Frequency (GHz)'], df['Gain (dB)'], label='Gain (dB)', color='blue')
-        ax.set_xlabel('Frequency (GHz)')
-        ax.set_ylabel('Gain (dB)')
-        ax.set_title('Gain vs Frequency')
-        ax.legend()
-        plt.close(fig)  # Close to avoid inline display
-        
-        # Send summary to Groq API for analysis
-        data_summary = f"""
-        The dataset contains simulation results for antennas. The frequency range is from 1 GHz to 10 GHz. 
-        - The antenna's gain increases from 5 dB to 30 dB as frequency increases. 
-        - Efficiency is consistently above 90%, with the highest reaching 99%.
-        """
-        
-        headers = {
-            "Authorization": f"Bearer {API_KEY}",
-            "Content-Type": "application/json"
-        }
+            # Send summary to Groq API for analysis
+            data_summary = f"""
+            The dataset contains simulation results for antennas. The frequency range is from 1 GHz to 10 GHz. 
+            - The antenna's gain increases from 5 dB to 30 dB as frequency increases. 
+            - Efficiency is consistently above 90%, with the highest reaching 99%.
+            """
+            
+            headers = {
+                "Authorization": f"Bearer {API_KEY}",
+                "Content-Type": "application/json"
+            }
 
-        payload = {
-            "messages": [{"role": "user", "content": data_summary}],
-            "model": "llama-3.3-70b-versatile"  # Ensure this model is supported by Groq
-        }
+            payload = {
+                "messages": [{"role": "user", "content": data_summary}],
+                "model": "llama-3.3-70b-versatile"  # Ensure this model is supported by Groq
+            }
 
-        response = requests.post(API_URL, json=payload, headers=headers)
-        if response.status_code == 200:
-            groq_analysis = response.json()["choices"][0]["message"]["content"]
+            # Send the request to Groq API
+            response = requests.post(API_URL, json=payload, headers=headers)
+            if response.status_code == 200:
+                groq_analysis = response.json()["choices"][0]["message"]["content"]
+            else:
+                groq_analysis = f"Error: {response.status_code}, {response.text}"
+
+            return mean_gain, median_gain, std_dev_gain, fig, groq_analysis
         else:
-            groq_analysis = f"Error: {response.status_code}, {response.text}"
-        
-        return mean_gain, median_gain, std_dev_gain, fig, groq_analysis
-    else:
-        return "Required columns 'Gain (dB)' or 'Frequency (GHz)' not found in the dataset."
+            return "Error: Required columns 'Gain (dB)' or 'Frequency (GHz)' not found in the dataset."
+
+    except Exception as e:
+        # Return error message if something goes wrong
+        return f"An error occurred: {str(e)}"
+
 
 # Create the Gradio interface using the new Gradio 3.x syntax
 iface = gr.Interface(