Update app.py

app.py

@@ -193,7 +193,7 @@ def get_summaries_by_sentiment(articles):
     return pos_sum, neg_sum, neutral_sum
 
 
-def comparative_analysis(pos_sum, neg_sum, neutral_sum, model_provider):
+def comparative_analysis(pos_sum, neg_sum, neutral_sum, model_provider = "Groq"):
     prompt = f"""
 Perform a detailed comparative analysis of the sentiment across three categories of articles (Positive, Negative, and Neutral) about a specific company. Address the following aspects:
 
@@ -248,7 +248,7 @@ Perform a detailed comparative analysis of the sentiment across three categories
     return response
 
 
-def generate_final_report(pos_sum, neg_sum, neutral_sum, comparative_sentiment, model_provider):
+def generate_final_report(pos_sum, neg_sum, neutral_sum, comparative_sentiment, model_provider = "Groq"):
     final_report_prompt = f"""
     Corporate News Sentiment Analysis Report:
 
@@ -332,7 +332,7 @@ def generate_final_report(pos_sum, neg_sum, neutral_sum, comparative_sentiment,
     return response
     
 
-def translate(report, model_provider):
+def translate(report, model_provider = "Groq"):
     translation_prompt = f"""
     Translate the following corporate sentiment analysis report into Hindi:
 
@@ -398,7 +398,7 @@ def text_to_speech(text):
 st.title("Company Sentiment Analyzer")
 
 company_name = st.text_input("Enter Company Name", "Tesla")
-model_provider = st.selectbox("Model Provider", options=["Ollama", "Groq"])
+
 
 if st.button("Fetch Sentiment Data"):
     try:
@@ -408,7 +408,7 @@ if st.button("Fetch Sentiment Data"):
             st.error("No sources found.")
         else:
             sentiment_output = [
-                analyze_sentiment(article, model_provider)
+                analyze_sentiment(article)
                 for article in web_results["sources"][:5]
             ]
 
@@ -423,10 +423,10 @@ if st.button("Fetch Sentiment Data"):
                 negative_summary,
                 neutral_summary,
                 comparative_sentiment,
-                model_provider,
+                
             )
 
-            hindi_translation = translate(final_report, model_provider)
+            hindi_translation = translate(final_report)
             audio_path = text_to_speech(hindi_translation)
 
             output_dict = {