Update app.py

app.py

@@ -3,7 +3,7 @@ import re
 import plotly.graph_objects as go
 import streamlit as st
 import json
-import google.generativeai as genai
+from openai import OpenAI
 import concurrent.futures
 import textstat
 import trafilatura
@@ -19,11 +19,16 @@ URL_A = "https://www.foxnews.com/live-news/trump-iran-israel-war-updates-march-3
 URL_B = "https://edition.cnn.com/2026/03/30/world/live-news/iran-war-us-israel-trump"
 
 # Initialize the AI model
-GEMINI_API_KEY = st.secrets.get("GEMINI_API_KEY")
-if GEMINI_API_KEY:
-    genai.configure(api_key=GEMINI_API_KEY)
+GROQ_API_KEY = st.secrets.get("GROQ_API_KEY")
 
-ai_model = genai.GenerativeModel('gemini-3.1-flash-lite-preview')
+if not GROQ_API_KEY:
+    st.warning("Groq API Token Missing.")
+    st.stop()
+
+client = OpenAI(
+    api_key=GROQ_API_KEY,
+    base_url="https://api.groq.com/openai/v1",
+)
 
 def _truncate_to_words(text: str, limit: int) -> str:
     """Truncates text by word count."""
@@ -51,14 +56,20 @@ def analyze_article(text: str) -> dict:
     Text to analyze:
     "{safe_text}"
     """
-    response = ai_model.generate_content(
-        prompt,
-        generation_config={
-            "response_mime_type": "application/json",
-            "temperature": 0.1, 
-        }
-    )
-    llm_data = json.loads(response.text)
+    try:
+        response = client.chat.completions.create(
+            model="llama-3.3-70b-versatile",
+            response_format={"type": "json_object"},
+            temperature=0.1,
+            messages=[
+                {"role": "system", "content": "You are a media analyst. You MUST respond with ONLY valid JSON matching the exact requested schema."},
+                {"role": "user", "content": prompt}
+            ]
+        )
+        llm_data = json.loads(response.choices[0].message.content)
+    except Exception as e:
+        print(f"Error parsing LLM response: {e}")
+        llm_data = {}
 
     raw_reading_ease = textstat.flesch_reading_ease(safe_text)
     
@@ -219,14 +230,20 @@ def check_contradiction(text_a: str, text_b: str) -> dict:
     Text 1: "{safe_a}"
     Text 2: "{safe_b}"
     """
-    response = ai_model.generate_content(
-        prompt,
-        generation_config={
-            "response_mime_type": "application/json",
-            "temperature": 0.1, 
-        }
-    )
-    result = json.loads(response.text)
+    try:
+        response = client.chat.completions.create(
+            model="llama-3.3-70b-versatile",
+            response_format={"type": "json_object"},
+            temperature=0.1,
+            messages=[
+                {"role": "system", "content": "You are a fact-checker. You MUST respond with ONLY valid JSON."},
+                {"role": "user", "content": prompt}
+            ]
+        )
+        result = json.loads(response.choices[0].message.content)
+    except Exception as e:
+        print(f"Error in contradiction check: {e}")
+        result = {}
     return {"relationship": result.get("relationship", "NEUTRAL"), "confidence": result.get("confidence", 0.0)}
     
 
@@ -328,11 +345,17 @@ if execute_analysis:
         st.error("One of the URLs could not be scraped. Please copy and paste the text directly.")
     
     else:
-        with st.spinner("Analyzing framing semantics for both sources (this takes a few seconds)..."):
+
+        with st.spinner("Analyzing both sources."):
             try:
-                st.session_state.results_a = analyze_article(text_a_clean)
-                st.session_state.results_b = analyze_article(text_b_clean)
-                st.session_state.nli_result = check_contradiction(text_a_clean, text_b_clean)
+                with concurrent.futures.ThreadPoolExecutor() as executor:
+                    future_a = executor.submit(analyze_article, text_a_clean)
+                    future_b = executor.submit(analyze_article, text_b_clean)
+                    future_nli = executor.submit(check_contradiction, text_a_clean, text_b_clean)
+                    
+                    st.session_state.results_a = future_a.result()
+                    st.session_state.results_b = future_b.result()
+                    st.session_state.nli_result = future_nli.result()
                 
             except Exception as e:
                 error_msg = str(e)