Update app.py

app.py

@@ -17,15 +17,15 @@ import fitz  # PyMuPDF
 from PIL import Image
 from dotenv import load_dotenv
 import torch
-import yfinance as yf  # Added missing import
-import plotly.graph_objects as go  # Added missing import
+import yfinance as yf
+import plotly.graph_objects as go
 
 # Assuming groq is a custom module or typo; replace with actual import if needed
-from groq import Client as GroqClient  # Placeholder; adjust based on your setup
+from groq import Client as GroqClient  # Adjust based on your setup
 
 # LangChain imports (optional, only if embeddings are available)
 try:
-    from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+    from langchain_community.embeddings import HuggingFaceEmbeddings  # Changed to basic embeddings
     from langchain_community.vectorstores import FAISS
     from langchain.text_splitter import RecursiveCharacterTextSplitter
     langchain_available = True
@@ -35,25 +35,18 @@ except ImportError:
 
 # Load environment variables
 load_dotenv()
-client = GroqClient(api_key=os.getenv("GROQ_LEGAL_API_KEY"))  # Adjust if groq is different
+client = GroqClient(api_key=os.getenv("GROQ_LEGAL_API_KEY"))
 
 # Embeddings initialization with fallback
 if langchain_available:
     try:
-        embeddings = HuggingFaceInstructEmbeddings(
-            model_name="hkunlp/instructor-base",
+        embeddings = HuggingFaceEmbeddings(
+            model_name="sentence-transformers/all-MiniLM-L6-v2",
             model_kwargs={"device": "cuda" if torch.cuda.is_available() else "cpu"}
         )
     except Exception as e:
-        print(f"Warning: Failed to load primary embeddings model: {e}")
-        try:
-            embeddings = HuggingFaceInstructEmbeddings(
-                model_name="all-MiniLM-L6-v2",
-                model_kwargs={"device": "cuda" if torch.cuda.is_available() else "cpu"}
-            )
-        except Exception as e:
-            print(f"Warning: Failed to load fallback embeddings model: {e}")
-            embeddings = None
+        print(f"Warning: Failed to load embeddings model: {e}")
+        embeddings = None
 else:
     embeddings = None
     print("Embeddings disabled due to missing LangChain dependencies.")
@@ -106,7 +99,7 @@ body { background-color: var(--light-color); font-family: 'IBM Plex Sans', sans-
 .search-toggle { margin-left: 5px; }
 """
 
-# Function to process PDF files
+# Function definitions
 def process_pdf(pdf_file):
     if pdf_file is None:
         return None, "No file uploaded", {"page_images": [], "total_pages": 0, "total_words": 0}
@@ -144,9 +137,96 @@ def process_pdf(pdf_file):
             os.unlink(pdf_path)
         return None, f"Error processing PDF: {str(e)}", {"page_images": [], "total_pages": 0, "total_words": 0}
 
-# [Rest of your functions remain unchanged up to the Gradio interface]
+def update_pdf_viewer(pdf_state):
+    if not pdf_state["total_pages"]:
+        return 0, None, "No PDF uploaded yet"
+    try:
+        img_data = base64.b64decode(pdf_state["page_images"][0])
+        img = Image.open(io.BytesIO(img_data))
+        return pdf_state["total_pages"], img, f"**Total Pages:** {pdf_state['total_pages']}\n**Total Words:** {pdf_state['total_words']}"
+    except Exception as e:
+        print(f"Error decoding image: {e}")
+        return 0, None, "Error displaying PDF"
+
+def update_image(page_num, pdf_state):
+    if not pdf_state["total_pages"] or page_num < 1 or page_num > pdf_state["total_pages"]:
+        return None
+    try:
+        img_data = base64.b64decode(pdf_state["page_images"][page_num - 1])
+        img = Image.open(io.BytesIO(img_data))
+        return img
+    except Exception as e:
+        print(f"Error decoding image: {e}")
+        return None
+
+def analyze_ticker(ticker, period, web_search_enabled):
+    try:
+        stock = yf.Ticker(ticker)
+        hist = stock.history(period=period)
+        if hist.empty:
+            return None, f"No data found for {ticker}", ticker
+        
+        fig = go.Figure()
+        fig.add_trace(go.Candlestick(
+            x=hist.index,
+            open=hist['Open'],
+            high=hist['High'],
+            low=hist['Low'],
+            close=hist['Close'],
+            name='OHLC'
+        ))
+        fig.update_layout(
+            title=f'{ticker} Stock Price ({period})',
+            yaxis_title='Price',
+            template='plotly_white'
+        )
+        
+        analysis = f"## {ticker} Analysis\n\n"
+        analysis += f"Latest Close: ${hist['Close'][-1]:.2f}\n"
+        analysis += f"52 Week High: ${hist['High'].max():.2f}\n"
+        analysis += f"52 Week Low: ${hist['Low'].min():.2f}\n"
+        
+        return fig, analysis, ticker
+    except Exception as e:
+        return None, f"Error analyzing ticker: {str(e)}", ticker
+
+def generate_response(message, session_id, model_name, history, ticker, web_search_enabled):
+    if not message:
+        return history
+    try:
+        context = ""
+        if session_id and session_id in user_vectorstores:
+            vectorstore = user_vectorstores[session_id]
+            docs = vectorstore.similarity_search(message, k=3)
+            if docs:
+                context = "\n\nRelevant information from uploaded document:\n" + "\n".join(f"- {doc.page_content}" for doc in docs)
+        
+        system_prompt = "You are a financial assistant specializing in analyzing markets, stocks, and financial documents."
+        system_prompt += " You can help with understanding financial data, analyzing stocks, and explaining financial concepts."
+        if ticker:
+            system_prompt += f" Current ticker in focus: {ticker}"
+        if context:
+            system_prompt += " Use the following context to answer the question if relevant: " + context
+        
+        completion = client.chat.completions.create(
+            model=model_name,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": message}
+            ],
+            temperature=0.5,
+            max_tokens=1024
+        )
+        response = completion.choices[0].message.content
+        history.append({"role": "user", "content": message})
+        history.append({"role": "assistant", "content": response})
+        return history
+    except Exception as e:
+        history.append({"role": "user", "content": message})
+        history.append({"role": "assistant", "content": f"Error generating response: {str(e)}"})
+        return history
 
-# Update the Gradio interface
+# Gradio interface
 with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
     current_session_id = gr.State(None)
     pdf_state = gr.State({"page_images": [], "total_pages": 0, "total_words": 0})
@@ -159,9 +239,7 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
     </div>
     """)
     
-    # Main container with all functionality in tabs
     with gr.Tabs() as main_tabs:
-        # Chat Assistant Tab
         with gr.TabItem("💬 Chat Assistant", id=0):
             with gr.Row():
                 with gr.Column(scale=1):
@@ -181,7 +259,7 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
                 show_copy_button=True,
                 elem_classes="chat-container",
                 container=True,
-                type="messages"  # Updated to use messages format
+                type="messages"
             )
             with gr.Row():
                 msg = gr.Textbox(
@@ -192,7 +270,6 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
                 send_btn = gr.Button("Send", scale=1)
             clear_btn = gr.Button("Clear Conversation")
 
-        # Document Analysis Tab
         with gr.TabItem("📄 Document Analysis", id=1):
             with gr.Row():
                 with gr.Column(scale=1):
@@ -217,10 +294,8 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
                             pdf_image = gr.Image(label="Document Page", type="pil")
                             stats_display = gr.Markdown(elem_classes="stats-box")
 
-        # Financial Tools Tab
         with gr.TabItem("🔍 Financial Tools", id=2):
             with gr.Tabs() as financial_tabs:
-                # Stock Analysis
                 with gr.TabItem("Stock Analysis"):
                     with gr.Row():
                         with gr.Column():
@@ -238,43 +313,19 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
                         stock_chart = gr.Plot(label="Stock Price Chart")
                     stock_analysis = gr.Markdown()
 
-                # Market News
-                with gr.TabItem("Market News"):
-                    news_ticker = gr.Textbox(
-                        label="Company/Ticker",
-                        placeholder="Enter company name or ticker symbol"
-                    )
-                    news_btn = gr.Button("Fetch News")
-                    news_results = gr.Markdown()
-
-                # Financial Report Analysis
-                with gr.TabItem("Report Analysis"):
-                    with gr.Row():
-                        with gr.Column():
-                            report_image = gr.File(
-                                label="Upload Financial Chart/Image",
-                                file_types=["image"],
-                                type="filepath"
-                            )
-                            analyze_report_btn = gr.Button("Analyze Image")
-                        with gr.Column():
-                            report_preview = gr.Image(label="Preview", type="pil")
-                    report_analysis = gr.Markdown()
-    
-    # Event Handlers (Example implementation)
-    def chat_handler(message, history, session_id, model, ticker, web_search):
-        # Convert tuple history to messages format if needed
-        if history and isinstance(history[0], tuple):
-            history = [{"role": "user" if i % 2 == 0 else "assistant", "content": msg} for i, msg in enumerate(sum(history, ()))]
-        response = generate_response(message, session_id, model, history, ticker, web_search)
-        return response
-
+    # Event Handlers
     send_btn.click(
-        fn=chat_handler,
-        inputs=[msg, chatbot, current_session_id, model_dropdown, current_ticker, web_search_toggle],
+        fn=generate_response,
+        inputs=[msg, current_session_id, model_dropdown, chatbot, current_ticker, web_search_toggle],
         outputs=[chatbot]
     )
-    clear_btn.click(lambda: [], outputs=[chatbot])
+    
+    clear_btn.click(
+        fn=lambda: [],
+        inputs=None,
+        outputs=[chatbot],
+        queue=False
+    )
 
     upload_button.click(
         fn=process_pdf,
@@ -286,19 +337,24 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
         outputs=[page_slider, pdf_image, stats_display]
     )
 
+    page_slider.change(
+        fn=update_image,
+        inputs=[page_slider, pdf_state],
+        outputs=[pdf_image]
+    )
+
     analyze_stock_btn.click(
         fn=analyze_ticker,
         inputs=[ticker_input, period_dropdown, web_search_toggle],
         outputs=[stock_chart, stock_analysis, current_ticker]
     )
 
-# Add footer with attribution
-gr.HTML("""
-<div style="text-align: center; margin-top: 20px; padding: 10px; color: #666; font-size: 0.8rem; border-top: 1px solid #eee;">
-    Created by Calvin Allen Crawford
-</div>
-""")
+    gr.HTML("""
+    <div style="text-align: center; margin-top: 20px; padding: 10px; color: #666; font-size: 0.8rem; border-top: 1px solid #eee;">
+        Created by Calvin Allen Crawford
+    </div>
+    """)
 
-# Launch the app
 if __name__ == "__main__":
+    demo = create_interface()
     demo.launch()