Update app.py

app.py

@@ -36,30 +36,23 @@ service_context = ServiceContext.from_defaults(
     node_parser=SentenceSplitter(chunk_size=1000, chunk_overlap=200)
 )
 
-def fetch_earnings_transcript(symbol: str) -> str:
+def fetch_annual_report_10k(symbol: str) -> str:
     """
-    Fetch the latest transcript for a company's earnings call.
+    Fetch the latest annual report on Form 10-K for a specific company.
+
     Args:
     - symbol (str): The stock ticker symbol (e.g., 'AAPL').
+
     Returns:
-    - str: The earnings call transcript or an error message.
+    - str: The entire JSON response as a string or an error message.
     """
-    transcript_url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?apikey={FMP_API_KEY}"
-    
+    current_year = datetime.datetime.now().year
+    url = f"https://financialmodelingprep.com/api/v4/financial-reports-json?symbol={symbol}&year={current_year}&period=FY&apikey={FMP_API_KEY}"
+
     try:
-        response = requests.get(transcript_url, timeout=10)
+        response = requests.get(url, timeout=10)
         response.raise_for_status()
-        transcript_data = response.json()
-        
-        if not transcript_data:
-            return f"No transcript available for {symbol}."
-        
-        # Extract the first available transcript
-        latest_transcript = transcript_data[0].get("content", "")
-        if not latest_transcript:
-            return f"No transcript content found for {symbol}."
-        
-        return latest_transcript
+        return response.text  # Return the full API response as a string
 
     except requests.exceptions.HTTPError as http_err:
         return f"HTTP error occurred: {http_err}"
@@ -70,26 +63,24 @@ def fetch_earnings_transcript(symbol: str) -> str:
 
 # Prompts
 summary_prompt = (
-    "You are a world-class financial analyst with extensive experience analyzing quarterly reports. "
-    "Give me a comprehensive summary of the earnings report. Focus on the Strategic Insights and Key Financial Figures. "
-    "Answer in extensive bullet points please."
+    "You are a world-class financial analyst with extensive experience analyzing annual reports. "
+    "Provide a comprehensive summary of the 10-K report. Focus on Strategic Insights, Key Financial Figures, and Risk Factors. "
+    "Answer in extensive bullet points, summarizing the company's performance, strengths, and weaknesses."
 )
 
 question_prompt = (
-    "You are a financial analyst with extensive experience analyzing quarterly reports. "
-    "Read the earnings call transcript and earnings presentation report and generate 10 questions focusing on the strategic insights and financial figures. "
-    "Ask questions that require precise answers and provide strategic insight into the company's financial and strategic performance, such as revenue growth, market trends, profit margins, and more. "
-    "Only ask questions that can be answered using the provided document, without making any assumptions or inferences beyond the text. "
-    "Please format the questions as a list with a simple '1. Question 1', '2. Question 2', etc. structure. "
-    "Unless retrievable from the documents, don't ask questions which cannot be compared to previous periods."
+    "You are a financial analyst with extensive experience analyzing annual reports. "
+    "Read the 10-K report and generate 10 strategic questions focusing on the company's performance, risks, and financial figures. "
+    "Ask questions that provide strategic insights into the company's long-term goals, revenue trends, competitive position, and more. "
+    "Format the questions as a numbered list (e.g., '1. Question')."
 )
 
 @cl.on_chat_start
 async def on_chat_start():
     ticker_response = await cl.AskUserMessage(
         content=(
-            "This tool is designed to analyze earnings call transcripts for publicly traded companies. "
-            "Provide the company's ticker symbol, and the tool will fetch the latest earnings call transcript. "
+            "This tool is designed to analyze 10-K annual reports for publicly traded companies. "
+            "Provide the company's ticker symbol, and the tool will fetch the latest available 10-K report. "
             "It generates summaries and strategic due diligence.\n\n"
             "Please enter the ticker symbol for the company you want to analyze:"
         )
@@ -97,23 +88,22 @@ async def on_chat_start():
 
     ticker_symbol = ticker_response['content'].upper()
 
-    msg = cl.Message(content=f"Retrieving earnings call transcript for {ticker_symbol}...")
+    msg = cl.Message(content=f"Retrieving the latest 10-K report for {ticker_symbol}...")
     await msg.send()
 
     try:
-        # Fetch the transcript using FMP API
-        transcript_text = fetch_earnings_transcript(ticker_symbol)
+        # Fetch the 10-K report using the adjusted function
+        annual_report_text = fetch_annual_report_10k(ticker_symbol)
 
         # Check if an error message was returned
-        if transcript_text.startswith("No transcript") or \
-           transcript_text.startswith("HTTP error") or \
-           transcript_text.startswith("Request error") or \
-           transcript_text.startswith("An unexpected error occurred"):
-            await cl.Message(content=transcript_text).send()
+        if annual_report_text.startswith("HTTP error") or \
+           annual_report_text.startswith("Request error") or \
+           annual_report_text.startswith("An unexpected error occurred"):
+            await cl.Message(content=annual_report_text).send()
             return
 
-        # Create a Document object with the transcript text
-        document = Document(text=transcript_text, metadata={"company": ticker_symbol})
+        # Create a Document object with the raw JSON response
+        document = Document(text=annual_report_text, metadata={"company": ticker_symbol})
 
         # Create index
         index = VectorStoreIndex.from_documents(
@@ -133,13 +123,12 @@ async def on_chat_start():
         questions_format = str(questions_response).split('\n')
         relevant_questions = [question.strip() for question in questions_format if question.strip() and question.strip()[0].isdigit()]
 
-        # Answer generated questions
-        await cl.Message(content="Generated questions and answers:").send()
+        # Display generated questions
+        await cl.Message(content="Generated strategic questions:").send()
         for question in relevant_questions:
-            response = await cl.make_async(query_engine.query)(question)
-            await cl.Message(content=f"**{question}**\n{response}").send()
+            await cl.Message(content=f"**{question}**").send()
 
-        msg.content = "Processing done. You can now ask more questions about the earnings call transcript!"
+        msg.content = "Processing done. You can now ask more questions about the 10-K report!"
         await msg.update()
 
     except Exception as e: