Update app.py

app.py

@@ -5,9 +5,9 @@ import plotly.graph_objects as go
 import streamlit as st
 from textblob import TextBlob
 import json
+import google.generativeai as genai
 import os
 import concurrent.futures
-from groq import Groq
 import textstat
 import trafilatura
 import requests
@@ -22,9 +22,12 @@ ARTICLE_B = """Tech industry leaders and economists are sounding the alarm over
 URL_A = "https://www.foxnews.com/live-news/trump-iran-israel-war-updates-march-30"
 URL_B = "https://edition.cnn.com/2026/03/30/world/live-news/iran-war-us-israel-trump"
 
-# Initialize the Hugging Face Client
-GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
-client = Groq(api_key=GROQ_API_KEY)
+# Initialize the AI model
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
+if GEMINI_API_KEY:
+    genai.configure(api_key=GEMINI_API_KEY)
+
+ai_model = genai.GenerativeModel('gemini-2.5-pro')
 
 def _truncate_to_words(text: str, limit: int) -> str:
     """Truncates text by word count."""
@@ -62,25 +65,33 @@ def analyze_article(text: str) -> dict:
     Text to analyze:
     "{safe_text}"
     """
-    
-    messages = [{"role": "user", "content": prompt}]
-    response = client.chat.completions.create(
-        model="llama-3.3-70b-versatile",
-        messages=messages,
-        max_tokens=300,
-        temperature=0.1,
-        response_format={"type": "json_object"}
+    response = ai_model.generate_content(
+        prompt,
+        generation_config={
+            "response_mime_type": "application/json",
+            "temperature": 0.1, 
+        }
     )
-    llm_data = json.loads(response.choices[0].message.content)
+    llm_data = json.loads(response.text)
 
     subjectivity_score = TextBlob(safe_text).sentiment.subjectivity
     raw_reading_ease = textstat.flesch_reading_ease(safe_text)
     
+    tones = llm_data.get("tone_scores", {})
+    standard_tones = {
+        "anger": tones.get("anger", 0.0),
+        "fear": tones.get("fear", 0.0),
+        "joy": tones.get("joy", 0.0),
+        "sadness": tones.get("sadness", 0.0),
+        "surprise": tones.get("surprise", 0.0),
+        "trust": tones.get("trust", 0.0),
+    }
+
     return {
         "sentiment_score": llm_data.get("sentiment_score", 0.0),
         "primary_tone": llm_data.get("primary_tone", "neutral"),
         "primary_theme": llm_data.get("primary_theme", "unclear"),
-        "tone_scores": llm_data.get("tone_scores", {"anger": 0, "fear": 0, "joy": 0, "sadness": 0, "surprise": 0, "trust": 0}),
+        "tone_scores": standard_tones, 
         "framing_words": llm_data.get("framing_words", []),
         "subjectivity_score": subjectivity_score,
         "reading_ease": max(0.0, min(100.0, raw_reading_ease)),
@@ -214,24 +225,22 @@ def check_contradiction(text_a: str, text_b: str) -> dict:
     Text 1: "{safe_a}"
     Text 2: "{safe_b}"
     """
-    messages = [{"role": "user", "content": prompt}]
-    response = client.chat.completions.create(
-        model="llama-3.3-70b-versatile",
-        messages=messages,
-        max_tokens=100,
-        temperature=0.1,
-        response_format={"type": "json_object"}
+    response = ai_model.generate_content(
+        prompt,
+        generation_config={
+            "response_mime_type": "application/json",
+            "temperature": 0.1, 
+        }
     )
-    
-    result = json.loads(response.choices[0].message.content)
+    result = json.loads(response.text)
     return {"relationship": result.get("relationship", "NEUTRAL"), "confidence": result.get("confidence", 0.0)}
     
 
 # USER INTERFACE
 st.set_page_config(page_title="FrameVis | Media Framing", layout="wide")
 
-if not GROQ_API_KEY:
-    st.warning("Groq API Token Missing.")
+if not GEMINI_API_KEY:
+    st.warning("Gemini API Token Missing.")
     st.stop()
 
 st.markdown("""