Update app.py

app.py

@@ -3,7 +3,6 @@ import streamlit as st
 import requests
 import datetime
 import time
-import json
 from dotenv import load_dotenv
 from tavily import TavilyClient
 import feedparser
@@ -20,6 +19,8 @@ TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
 tavily = TavilyClient(api_key=TAVILY_API_KEY)
 
 # --- Helper Functions ---
+import json
+
 def call_llm(messages, model="deepseek/deepseek-chat-v3-0324:free", max_tokens=3500, temperature=0.7):
     url = "https://openrouter.ai/api/v1/chat/completions"
     headers = {
@@ -34,7 +35,9 @@ def call_llm(messages, model="deepseek/deepseek-chat-v3-0324:free", max_tokens=3
         "temperature": temperature,
         "stream": True
     }
+
     with requests.post(url, headers=headers, json=data, stream=True) as response:
+        content = ""
         for line in response.iter_lines():
             if line:
                 decoded = line.decode("utf-8")
@@ -46,10 +49,12 @@ def call_llm(messages, model="deepseek/deepseek-chat-v3-0324:free", max_tokens=3
                             delta = parsed.get("choices", [{}])[0].get("delta", {})
                             token = delta.get("content", "")
                             if token:
-                                yield token
+                                content += token
+                                yield token  # Yield only the new token, not full content each time
                         except json.JSONDecodeError:
                             pass
 
+
 def get_sources(topic, domains=None):
     query = topic
     if domains:
@@ -139,7 +144,6 @@ def generate_download_button(file, label, mime_type):
         </a>
     """
 
-# --- Streamlit UI ---
 st.set_page_config("Deep Research Bot", layout="centered")
 st.markdown("""
     <style>
@@ -186,15 +190,6 @@ if research_button and topic:
                 raise ValueError("Unable to fetch any sources. Please try again later.")
 
             merged = merge_duplicates(all_sources)
-
-            st.markdown("---")
-            st.subheader("🖼 Source Previews")
-            cols = st.columns(2)
-            for i, m in enumerate(merged):
-                if m.get("image_url"):
-                    with cols[i % 2]:
-                        st.image(m["image_url"], caption=m["title"], use_column_width=True)
-
             citations = [generate_apa_citation(m['title'], m['url'], m['source']) for m in merged]
             combined_text = "\n\n".join(
                 [f"- [{m['title']}]({m['url']})\n> {m.get('snippet', m.get('summary', ''))[:300]}..." for m in merged]
@@ -203,10 +198,8 @@ if research_button and topic:
 
         prompt = f"""
 You are an expert assistant. Based on the following sources, write a {report_type.lower()} in a {tone.lower()} tone on the topic: {topic}.
-
 Sources:
 {combined_text}
-
 APA Citations:
 {chr(10).join(citations)}
         """