Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import requests
+from dotenv import load_dotenv
+import os
+
+# Load environment variables
+load_dotenv()
+API_KEY = os.getenv("GEMINI_API_KEY")
+
+# Function to get column suggestions from Gemini API
+def ask_gemini_for_columns_and_graph(api_key, df, user_query):
+    """
+    Use Gemini API to determine the best columns and graph type based on the user's query.
+    """
+    columns = df.columns.tolist()
+
+    prompt = f"""
+    You are analyzing a CSV file with the following columns: {columns}.
+    Based on the user's query: "{user_query}",
+    suggest two columns: one for the X-axis and one for the Y-axis, and the most suitable graph type (e.g., bar, scatter, line, histogram, pie). 
+    Respond in JSON format like this:
+    {{
+        "x_column": "ColumnX",
+        "y_column": "ColumnY",
+        "graph_type": "graphType"
+    }}
+    """
+
+    payload = {
+        "contents": [
+            {
+                "parts": [{"text": prompt}]
+            }
+        ]
+    }
+
+    url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent?key={api_key}"
+    headers = {"Content-Type": "application/json"}
+
+    try:
+        response = requests.post(url, json=payload, headers=headers)
+        response.raise_for_status()
+        content = response.json()
+        reply = content['candidates'][0]['content']['parts'][0]['text'].strip()
+        result = eval(reply.replace("```json", "").replace("```", "").strip())
+        return result['x_column'], result['y_column'], result['graph_type']
+    except Exception as e:
+        st.error(f"Error interacting with the Gemini API: {e}")
+        return None, None, None
+
+# Function to plot the graph
+def plot_graph(df, x_column, y_column, graph_type):
+    plt.figure(figsize=(10, 6))
+    try:
+        if graph_type == "bar":
+            plt.bar(df[x_column], df[y_column], color='skyblue')
+            plt.xlabel(x_column)
+            plt.ylabel(y_column)
+            plt.title(f"Bar Graph: {y_column} vs {x_column}")
+            plt.xticks(rotation=45)
+        elif graph_type == "scatter":
+            plt.scatter(df[x_column], df[y_column], color='skyblue')
+            plt.xlabel(x_column)
+            plt.ylabel(y_column)
+            plt.title(f"Scatter Plot: {y_column} vs {x_column}")
+        elif graph_type == "line":
+            plt.plot(df[x_column], df[y_column], color='skyblue', marker='o')
+            plt.xlabel(x_column)
+            plt.ylabel(y_column)
+            plt.title(f"Line Graph: {y_column} vs {x_column}")
+        elif graph_type == "histogram":
+            plt.hist(df[y_column], bins=20, color='skyblue', edgecolor='black')
+            plt.xlabel(y_column)
+            plt.ylabel("Frequency")
+            plt.title(f"Histogram of {y_column}")
+        elif graph_type == "pie":
+            pie_data = df[x_column].value_counts()
+            plt.pie(pie_data, labels=pie_data.index, autopct='%1.1f%%', startangle=90, colors=plt.cm.Paired.colors)
+            plt.title(f"Pie Chart: Distribution of {x_column}")
+        else:
+            st.error(f"Unsupported graph type: {graph_type}")
+            return
+        st.pyplot(plt)
+    except Exception as e:
+        st.error(f"Error generating the plot: {e}")
+
+# Streamlit Application
+def main():
+    st.title("Interactive Graph Generator")
+
+    # File upload
+    uploaded_file = st.file_uploader("Upload your CSV file", type="csv")
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+        st.dataframe(df)
+
+        # User query input
+        user_query = st.text_input("Describe the graph you'd like to generate:")
+
+        if st.button("Generate Graph"):
+            # Get graph suggestions from Gemini API
+            x_column, y_column, graph_type = ask_gemini_for_columns_and_graph(API_KEY, df, user_query)
+
+            if x_column and graph_type:
+                plot_graph(df, x_column, y_column, graph_type)
+            else:
+                st.error("Could not determine columns or graph type.")
+
+if __name__ == "__main__":
+    main()