Update app.py

app.py

@@ -35,33 +35,38 @@ def call_llm(messages, model="deepseek/deepseek-chat-v3-0324:free", max_tokens=3
 
     response = requests.post(url, headers=headers, json=data)
 
+    try:
+        result = response.json()
+    except Exception:
+        raise RuntimeError(f"LLM API returned invalid JSON: {response.text}")
+
     if response.status_code != 200:
-        try:
-            error_msg = response.json().get("error", {}).get("message", "Unknown error from LLM API.")
-        except Exception:
-            error_msg = response.text
+        error_msg = result.get("error", {}).get("message", "Unknown error from LLM API.")
         raise RuntimeError(f"LLM API error: {error_msg}")
 
-    try:
-        result = response.json()
-        return result["choices"][0]["message"]["content"]
-    except KeyError:
-        raise RuntimeError("LLM API returned unexpected response format. Missing 'choices'.")
+    if "choices" not in result:
+        raise RuntimeError(f"LLM API returned unexpected response format. Full response:\n{result}")
+
+    return result["choices"][0]["message"]["content"]
 
 def get_sources(topic, domains=None):
     query = topic
     if domains:
         domain_filters = [d.strip() for d in domains.split(",") if d.strip()]
         query += " site:" + " OR site:".join(domain_filters)
-    response = tavily.search(query=query, search_depth="advanced", max_results=10)
-    sources = []
-    for item in response.get("results", []):
-        sources.append({
-            "title": item.get("title"),
-            "url": item.get("url"),
-            "snippet": item.get("content", "")
-        })
-    return sources
+    try:
+        response = tavily.search(query=query, search_depth="advanced", max_results=10)
+        sources = []
+        for item in response.get("results", []):
+            sources.append({
+                "title": item.get("title"),
+                "url": item.get("url"),
+                "snippet": item.get("content", "")
+            })
+        return sources
+    except Exception as e:
+        st.error(f"Error retrieving sources: {e}")
+        return []
 
 def get_arxiv_papers(query):
     from urllib.parse import quote_plus
@@ -130,8 +135,8 @@ def generate_latex(text):
 def generate_download_button(file, label, mime_type):
     b64 = base64.b64encode(file.read()).decode()
     return f"""
-        <a href="data:{mime_type};base64,{b64}" download="{label}">
-            📥 Download {label}
+        <a href=\"data:{mime_type};base64,{b64}\" download=\"{label}\">
+            \ud83d\udce5 Download {label}
         </a>
     """
 
@@ -143,27 +148,27 @@ def fetch_related_image(topic):
 st.set_page_config("Deep Research Bot", layout="wide")
 
 with st.sidebar:
-    st.title("🧠 Deep Research Assistant")
-    topic = st.text_input("💡 Topic to research")
-    report_type = st.selectbox("📄 Type of report", [
+    st.title("\ud83e\udde0 Deep Research Assistant")
+    topic = st.text_input("\ud83d\udca1 Topic to research")
+    report_type = st.selectbox("\ud83d\udcc4 Type of report", [
         "Summary - Short and fast (~2 min)",
         "Detailed Report (~5 min)",
         "Thorough Academic Research (~10 min)"
     ])
-    tone = st.selectbox("🎯 Tone of the report", [
+    tone = st.selectbox("\ud83c\udfaf Tone of the report", [
         "Objective - Impartial and unbiased presentation of facts and findings",
         "Persuasive - Advocating a specific point of view",
         "Narrative - Storytelling tone for layperson readers"
     ])
-    source_type = st.selectbox("🌐 Sources to include", ["Web Only", "Academic Only", "Hybrid"])
-    custom_domains = st.text_input("🔍 Query Domains (Optional)", placeholder="techcrunch.com, forbes.com")
-    research_button = st.button("🚀 Start Research")
+    source_type = st.selectbox("\ud83c\udf10 Sources to include", ["Web Only", "Academic Only", "Hybrid"])
+    custom_domains = st.text_input("\ud83d\udd0d Query Domains (Optional)", placeholder="techcrunch.com, forbes.com")
+    research_button = st.button("\ud83d\ude80 Start Research")
 
-st.title("📑 Research Output")
+st.title("\ud83d\udcc1 Research Output")
 
 if research_button and topic:
     try:
-        with st.status("🔍 Gathering data..."):
+        with st.status("\ud83d\udd0d Gathering data..."):
             st.info("Fetching from sources...")
 
             all_sources = []
@@ -188,7 +193,7 @@ if research_button and topic:
                 combined_text += f"- [{m['title']}]({m['url']})\n> {m.get('snippet', m.get('summary', ''))[:300]}...\n\n"
                 citations.append(generate_apa_citation(m['title'], m['url'], m['source']))
 
-        with st.spinner("✍️ Synthesizing report..."):
+        with st.spinner("\u270d\ufe0f Synthesizing report..."):
             prompt = f"""
 # Research Topic: {topic}
 Tone: {tone}
@@ -204,19 +209,19 @@ Write the report in academic markdown with paragraphs (use bullet points only wh
 """
             final_output = call_llm([{"role": "user", "content": prompt}])
 
-        st.markdown(f"### 📄 {report_type}")
+        st.markdown(f"### \ud83d\udcc4 {report_type}")
         st.markdown(final_output, unsafe_allow_html=True)
 
         if report_type == "Thorough Academic Research (~10 min)":
             image_url = fetch_related_image(topic)
             st.image(image_url, caption="Related Image", use_column_width=True)
 
-        st.markdown("### 📚 Citations (APA Format)")
+        st.markdown("### \ud83d\udcda Citations (APA Format)")
         for cite in citations:
             st.markdown(f"- {cite}")
 
         if report_type == "Thorough Academic Research (~10 min)":
-            with st.spinner("📦 Preparing PDF and LaTeX..."):
+            with st.spinner("\ud83d\udce6 Preparing PDF and LaTeX..."):
                 pdf_file = generate_pdf(final_output)
                 latex_file = generate_latex(final_output)
                 st.markdown(generate_download_button(pdf_file, "Research_Report.pdf", "application/pdf"), unsafe_allow_html=True)
@@ -224,11 +229,11 @@ Write the report in academic markdown with paragraphs (use bullet points only wh
 
         overlaps = check_plagiarism(final_output, topic)
         if overlaps:
-            st.warning("⚠️ Potential overlaps detected:")
+            st.warning("\u26a0\ufe0f Potential overlaps detected:")
             for hit in overlaps:
                 st.markdown(f"- [{hit['title']}]({hit['url']})")
         else:
-            st.success("✅ No major overlaps found.")
+            st.success("\u2705 No major overlaps found.")
 
     except Exception as e:
-        st.error(f"Error: {e}")
+        st.error(f"Error: {e}")