Update app.py

app.py

@@ -52,13 +52,39 @@ def analyze_sentiment(text):
     result = sentiment_model(text)[0]
     return result['label'], result['score']
 
+# Function to fetch stock data from Alpha Vantage API
+def fetch_stock_data(company_name):
+    url = "https://alpha-vantage.p.rapidapi.com/query"
+    querystring = {"function": "TIME_SERIES_DAILY", "symbol": company_name, "outputsize": "compact", "datatype": "json"}
+    headers = {
+        "x-rapidapi-key": "e078dae417mshb13ddc2d8149768p1608e9jsn888ce49e8554",
+        "x-rapidapi-host": "alpha-vantage.p.rapidapi.com"
+    }
+    
+    try:
+        response = requests.get(url, headers=headers, params=querystring)
+        response.raise_for_status()
+        data = response.json()
+    except requests.RequestException as e:
+        print(f"Error fetching stock data: {e}")
+        return None
+    
+    if "Time Series (Daily)" not in data:
+        return None
+    
+    stock_data = data["Time Series (Daily)"]
+    df = pd.DataFrame.from_dict(stock_data, orient='index')
+    df.index = pd.to_datetime(df.index)
+    df = df.astype(float)
+    return df
+
 # Main function to process news and perform analysis
 def news_and_analysis(query):
     # Fetch news
     news_df = fetch_news(query)
     
     if news_df.empty:
-        return "No news articles found.", None
+        return "No news articles found.", None, None
     
     # Perform sentiment analysis
     news_df['Sentiment'], news_df['Sentiment_Score'] = zip(*news_df['Title'].apply(analyze_sentiment))
@@ -74,7 +100,19 @@ def news_and_analysis(query):
         labels={'Time': 'Publication Time', 'Sentiment_Score': 'Sentiment Score'}
     )
     
-    return news_df, sentiment_fig
+    # Check if the input query is a company name (heuristic: if it's more than one word)
+    if len(query.split()) > 1:
+        stock_df = fetch_stock_data(query)
+        if stock_df is not None:
+            stock_fig = px.line(
+                stock_df,
+                x=stock_df.index,
+                y='4. close',
+                title=f'{query} Stock Price Over Time',
+                labels={'index': 'Date', '4. close': 'Closing Price'}
+            )
+            return news_df, sentiment_fig, stock_fig
+    return news_df, sentiment_fig, None
 
 # Gradio interface
 with gr.Blocks() as demo:
@@ -95,11 +133,12 @@ with gr.Blocks() as demo:
         with gr.Column():
             news_output = gr.DataFrame(label="News and Sentiment Analysis")
             sentiment_plot = gr.Plot(label="Sentiment Analysis")
+            stock_plot = gr.Plot(label="Stock Price Analysis")
     
     analyze_btn.click(
         news_and_analysis,
         inputs=[topic],
-        outputs=[news_output, sentiment_plot]
+        outputs=[news_output, sentiment_plot, stock_plot]
     )
 
 if __name__ == "__main__":