Update app.py

app.py

@@ -14,6 +14,7 @@ import base64
 from xhtml2pdf import pisa
 import logging
 import json
+import streamlit.components.v1 as components
 
 # Load environment variables
 load_dotenv()
@@ -408,23 +409,24 @@ def get_unique_output_dir(base_dir, ticker):
     return output_dir
 
 def get_stock_symbols(file_path):
-      """
-      Reads stock symbols from a JSON file.
-
-      Args:
-          file_path (str): The path to the JSON file.
-
-      Returns:
-          list: A list of stock symbols.
-      """
-      try:
-          with open(file_path, 'r') as f:
-              data = json.load(f)
-              symbols = [stock["symbol"] for stock in data.values()]
-              return symbols
-      except Exception as e:
-          st.error(f"Error reading stock symbols: {e}")
-          return []
+    """
+    Reads stock symbols from a JSON file.
+
+    Args:
+        file_path (str): The path to the JSON file.
+
+    Returns:
+        list: A list of stock symbols.
+    """
+    try:
+        with open(file_path, 'r') as f:
+            data = json.load(f)
+        # Extract symbols directly from the JSON structure
+        symbols = [details["symbol"] for details in data.values()]
+        return symbols
+    except Exception as e:
+        st.error(f"Error reading stock symbols: {e}")
+        return []
 
 # --------------------- Streamlit App ---------------------
 
@@ -447,87 +449,205 @@ def main():
         except Exception as e:
             st.error(f"An error occurred while processing the uploaded file: {e}")
 
-    # Dropdown to select the stock
-    selected_stock = st.selectbox("Select a stock for analysis:", stock_symbols)
+    # Use the uploaded symbols for dynamic selection
+    selected_symbol = st.sidebar.selectbox("Select a stock for analysis:", stock_symbols)
 
     # Period selection
-    period = st.selectbox("Select the time period for analysis:", ["1d", "5d", "1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"])
-
-    if st.button("Analyze"):
-        with st.spinner("Performing technical analysis..."):
-            try:
-                # Create a unique output directory
-                output_dir = get_unique_output_dir(Config.OUTPUT_DIR, selected_stock)
-
-                # Fetch and process data
-                stock_data = fetch_data(selected_stock, period=period)
-                stock_data = calculate_moving_averages(stock_data)
-                stock_data = calculate_ema(stock_data)
-                stock_data = calculate_macd(stock_data)
-                stock_data = calculate_rsi(stock_data)
-                stock_data = calculate_adx(stock_data)
-                stock_data = calculate_atr(stock_data)
-                stock_data = calculate_bollinger_bands(stock_data)
-                stock_data = calculate_stochastic(stock_data)
-
-                # Get analysis output
-                analysis_output = print_indicator_outputs(stock_data, selected_stock, output_dir)
-
-                # Plot and save charts
-                plot_stock_and_indicators(stock_data, selected_stock, output_dir)
-                plot_volatility_indicators(stock_data, selected_stock, output_dir)
-
-                # Generate prompt for LLM
-                image_paths = [os.path.join(output_dir, file) for file in os.listdir(output_dir) if file.endswith(('.png', '.jpg', '.jpeg'))]
-                image_paths = "\n".join(image_paths)
-                prompt = generate_prompt(analysis_output, image_paths)
-
-                # Save the prompt to a file
-                prompt_filename = os.path.join(output_dir, f"{selected_stock}_prompt.txt")
-                with open(prompt_filename, 'w') as f:
-                    f.write(prompt)
-                logging.info(f"Saved LLM prompt to: {prompt_filename}")
-
-                # Configure and create LLM model
-                genai.configure(api_key=Config.GOOGLE_API_KEY)
-                generation_config = {
-                    "temperature": 0.9,
-                    "top_p": 0.95,
-                    "top_k": 40,
-                    "max_output_tokens": 8192,
-                }
-                model = genai.GenerativeModel(
-                    model_name="gemini-pro",  # Use "gemini-pro" for text-only
-                    generation_config=generation_config,
-                )
-                chat_session = model.start_chat()
-
-                # Get LLM response
-                llm_response = get_response(chat_session, prompt)
-
-                if llm_response:
-                    # Generate PDF
-                    pdf_filename = os.path.join(output_dir, f"technical_analysis_{selected_stock}.pdf")
-                    markdown_to_pdf_xhtml2pdf(llm_response.text, pdf_filename)
-
-                    # Offer PDF for download
-                    with open(pdf_filename, "rb") as pdf_file:
-                        pdf_bytes = pdf_file.read()
-                    st.download_button(
-                        label="Download Analysis PDF",
-                        data=pdf_bytes,
-                        file_name=f"technical_analysis_{selected_stock}.pdf",
-                        mime="application/pdf"
+    period = st.sidebar.selectbox("Select the time period for analysis:", ["1d", "5d", "1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"])
+
+    # Button to trigger analysis
+    if st.sidebar.button("Analyze"):
+        if selected_symbol:  # Check if a symbol is selected
+            with st.spinner("Performing technical analysis..."):
+                try:
+                    # Create a unique output directory
+                    output_dir = get_unique_output_dir(Config.OUTPUT_DIR, selected_symbol)
+
+                    # Fetch and process data
+                    stock_data = fetch_data(selected_symbol, period=period)
+                    stock_data = calculate_moving_averages(stock_data)
+                    stock_data = calculate_ema(stock_data)
+                    stock_data = calculate_macd(stock_data)
+                    stock_data = calculate_rsi(stock_data)
+                    stock_data = calculate_adx(stock_data)
+                    stock_data = calculate_atr(stock_data)
+                    stock_data = calculate_bollinger_bands(stock_data)
+                    stock_data = calculate_stochastic(stock_data)
+
+                    # Get analysis output
+                    analysis_output = print_indicator_outputs(stock_data, selected_symbol, output_dir)
+
+                    # Plot and save charts
+                    plot_stock_and_indicators(stock_data, selected_symbol, output_dir)
+                    plot_volatility_indicators(stock_data, selected_symbol, output_dir)
+
+                    # Generate prompt for LLM
+                    image_paths = [os.path.join(output_dir, file) for file in os.listdir(output_dir) if file.endswith(('.png', '.jpg', '.jpeg'))]
+                    image_paths = "\n".join(image_paths)
+                    prompt = generate_prompt(analysis_output, image_paths)
+
+                    # Save the prompt to a file
+                    prompt_filename = os.path.join(output_dir, f"{selected_symbol}_prompt.txt")
+                    with open(prompt_filename, 'w') as f:
+                        f.write(prompt)
+                    logging.info(f"Saved LLM prompt to: {prompt_filename}")
+
+                    # Configure and create LLM model
+                    genai.configure(api_key=Config.GOOGLE_API_KEY)
+                    generation_config = {
+                        "temperature": 0.9,
+                        "top_p": 0.95,
+                        "top_k": 40,
+                        "max_output_tokens": 8192,
+                    }
+                    model = genai.GenerativeModel(
+                        model_name="gemini-pro",  # Use "gemini-pro" for text-only
+                        generation_config=generation_config,
                     )
-                    # Display the PDF using an iframe
-                    b64 = base64.b64encode(pdf_bytes).decode()
-                    pdf_display = f'<iframe src="data:application/pdf;base64,{b64}" width="700" height="1000" type="application/pdf"></iframe>'
-                    st.markdown(pdf_display, unsafe_allow_html=True)
-                else:
-                    st.error("Could not generate PDF. LLM response is empty.")
-
-            except Exception as e:
-                st.error(f"An error occurred during analysis: {e}")
+                    chat_session = model.start_chat()
+
+                    # Get LLM response
+                    llm_response = get_response(chat_session, prompt)
+
+                    if llm_response:
+                        # Generate PDF
+                        pdf_filename = os.path.join(output_dir, f"technical_analysis_{selected_symbol}.pdf")
+                        markdown_to_pdf_xhtml2pdf(llm_response.text, pdf_filename)
+
+                        # Offer PDF for download
+                        with open(pdf_filename, "rb") as pdf_file:
+                            pdf_bytes = pdf_file.read()
+                        st.download_button(
+                            label="Download Analysis PDF",
+                            data=pdf_bytes,
+                            file_name=f"technical_analysis_{selected_symbol}.pdf",
+                            mime="application/pdf"
+                        )
+                        # Display the PDF using an iframe
+                        b64 = base64.b64encode(pdf_bytes).decode()
+                        pdf_display = f'<iframe src="data:application/pdf;base64,{b64}" width="700" height="1000" type="application/pdf"></iframe>'
+                        st.markdown(pdf_display, unsafe_allow_html=True)
+                    else:
+                        st.error("Could not generate PDF. LLM response is empty.")
+
+                except Exception as e:
+                    st.error(f"An error occurred during analysis: {e}")
+        else:
+            st.sidebar.error("Please select a stock symbol.")
+
+    # Placeholder for the TradingView widget
+    st.header("TradingView Widget")
+    tradingview_placeholder = st.empty()  # Create an empty placeholder
+
+    # --- TradingView Widget Code ---
+    tradingview_widget_code = f"""
+        <!-- TradingView Widget BEGIN -->
+        <div class="tradingview-widget-container" style="height:100%; width:100%">
+        <div id="tradingview_a1b2c" style="height:calc(100% - 32px); width:100%"></div>
+        <div class="tradingview-widget-copyright">
+            <a href="https://www.tradingview.com/" rel="noopener nofollow" target="_blank">
+            <span class="blue-text">Track all markets on TradingView</span>
+            </a>
+        </div>
+        <script type="text/javascript" src="https://s3.tradingview.com/tv.js"></script>
+        <script type="text/javascript">
+        new TradingView.widget(
+        {{
+        "autosize": true,
+        "symbol": "{selected_symbol}",
+        "interval": "D",
+        "timezone": "Etc/UTC",
+        "theme": "light",
+        "style": "1",
+        "locale": "en",
+        "toolbar_bg": "#f1f3f6",
+        "enable_publishing": false,
+        "allow_symbol_change": true,
+        "container_id": "tradingview_a1b2c"
+        }}
+        );
+        </script>
+        </div>
+        <!-- TradingView Widget END -->
+        """
+    # ----------------------------------
+
+    # HTML, CSS, and JavaScript for Resizing (outside the if condition)
+    html_code = f"""
+    <style>
+    /* Make the container resizable */
+    #resizable-container {{
+    position: relative;
+    width: 700px; /* Initial width */
+    height: 600px; /* Initial height */
+    border: 2px solid #ccc;
+    overflow: hidden; /* Important for clipping the widget during resize */
+    }}
+
+    /* Style the resize handle (optional) */
+    .resize-handle {{
+    position: absolute;
+    width: 10px;
+    height: 10px;
+    background-color: #007bff; /* Example color */
+    cursor: se-resize; /* Diagonal resize cursor */
+    }}
+
+    /* Position the resize handle at the bottom-right corner */
+    #resize-handle-se {{
+    bottom: 0;
+    right: 0;
+    }}
+    </style>
+
+    <div id="resizable-container">
+    {tradingview_widget_code}
+    <div id="resize-handle-se" class="resize-handle"></div>
+    </div>
+
+    <script src="https://cdn.jsdelivr.net/npm/interactjs/dist/interact.min.js"></script>
+    <script>
+    // Target the resize handle
+    interact('#resize-handle-se')
+    .draggable({{
+        // Restrict movement to the bounds of the container
+        modifiers: [
+        interact.modifiers.restrictRect({{
+            restriction: 'parent',
+            endOnly: true
+        }})
+        ],
+        inertia: true,
+    }})
+    .on('dragmove', function (event) {{
+        const container = document.getElementById('resizable-container');
+
+        // Update width and height based on drag movement
+        let newWidth = container.offsetWidth + event.dx;
+        let newHeight = container.offsetHeight + event.dy;
+
+        // Set minimum dimensions (adjust as needed)
+        const minWidth = 300;
+        const minHeight = 300;
+        if (newWidth < minWidth) {{
+            newWidth = minWidth;
+        }}
+        if (newHeight < minHeight) {{
+            newHeight = minHeight;
+        }}
+
+        container.style.width = newWidth + 'px';
+        container.style.height = newHeight + 'px';
+
+        // Trigger TradingView resize (if necessary - see note below)
+        // window.dispatchEvent(new Event('resize')); // You might not need this
+    }});
+    </script>
+    """
+
+    # Embed the HTML code using components.html
+    with tradingview_placeholder:
+      components.html(html_code, height=600, width=700, scrolling=False)
 
 if __name__ == "__main__":
     main()