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.streamlit/secrets.toml

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+OPENROUTER_API_KEY = "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"

main.py

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+#--------------------IMPORTED LIBRARIES-----------------------------
+
+import streamlit as st
+import base64
+import json
+import faiss
+import torch
+from transformers import AutoTokenizer, AutoModel
+import torch.nn.functional as F
+import httpx
+from huggingface_hub import hf_hub_download
+
+# ---------------------- INITIAL CONFIGURATION ----------------------
+
+st.set_page_config(page_title="PoliticBot", layout="wide")
+
+with open("fondo.jpeg", "rb") as f:
+    img_bytes = f.read()
+    encoded_img = base64.b64encode(img_bytes).decode()
+
+st.markdown(f"""
+    <style>
+    .stApp {{
+        background-image: url("data:image/jpeg;base64,{encoded_img}");
+        background-size: cover;
+        background-repeat: no-repeat;
+        background-attachment: fixed;
+    }}
+    </style>
+""", unsafe_allow_html=True)
+
+# -------------------------- STYLE CUSTOMIZATION-------------------------
+
+st.markdown("""
+    <style>
+    section[data-testid="stSidebar"] {
+        background-color: rgba(0, 0, 50, 0.6);
+        color: white;
+    }
+    
+    h1, h2, h3, h4, h5, h6, p, label, div, span {
+        color: white !important;
+    }
+    textarea {
+        color: white !important;
+        background-color: rgba(0, 0, 0, 0.3) !important;
+        border: 1px solid #ccc !important;
+        border-radius: 8px !important;
+        padding: 0.5em !important;
+    }
+    ::placeholder {
+        color: #ccc !important;
+    }
+    pre, code {
+        background-color: rgba(0, 0, 0, 0.4) !important;
+        color: white !important;
+        border-radius: 8px !important;
+        padding: 0.5em !important;
+    }
+    /* SOLO APLICA A BOTONES DEL SIDEBAR */
+    section[data-testid="stSidebar"] div[data-testid="stButton"] > button {
+        background-color: #526366 !important;
+        color: white !important;
+        font-weight: bold;
+        font-size: 16px;
+        border-radius: 8px;
+        padding: 0.6em;
+        width: 80% !important;
+        margin-bottom: 0.5em;
+    }
+    /* APLICA A BOTONES FUERA DEL SIDEBAR (ej: Send question) */
+    div[data-testid="stButton"] > button {
+        background-color: #526366 !important;
+        color: white !important;
+        font-weight: bold;
+        font-size: 16px;
+        border-radius: 8px;
+        padding: 0.6em;
+        margin-top: 1em;
+    }        
+    </style>
+""", unsafe_allow_html=True)
+
+# ---------------------- LIBRARIES AND MODELS ----------------------
+
+ideology_families = ["Communism", "Liberalism", "Conservatism", "Fascism", "Radical_Left"]
+
+ideology_keywords = {
+    "Communism": ["communism", "marxism", "marxist", "anarcho-communism", "leninism"],
+    "Liberalism": ["liberalism", "libertarianism", "classical liberal"],
+    "Conservatism": ["conservatism", "traditional conservatism", "neoconservatism"],
+    "Fascism": ["fascism", "nazism", "national socialism"],
+    "Radical_Left": ["radical left", "far-left", "revolutionary socialism", "anarchism"]
+}
+
+@st.cache_resource
+def load_encoder():
+    model_name = "intfloat/e5-base-v2"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModel.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")
+    return tokenizer, model
+
+tokenizer, model = load_encoder()
+
+def mean_pooling(output, mask):
+    token_embeddings = output.last_hidden_state
+    input_mask_expanded = mask.unsqueeze(-1).expand(token_embeddings.size())
+    return (token_embeddings * input_mask_expanded).sum(1) / input_mask_expanded.sum(1)
+
+def embed_query(query):
+    prefixed = f"query: {query}"
+    inputs = tokenizer(prefixed, return_tensors='pt', truncation=True, padding=True, max_length=512)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    with torch.no_grad():
+        outputs = model(**inputs)
+    pooled = mean_pooling(outputs, inputs["attention_mask"])
+    return F.normalize(pooled, p=2, dim=1).cpu().numpy().astype("float32")
+
+@st.cache_resource
+def load_data_global():
+    chunks_path = hf_hub_download(repo_id="Bartix84/politicbot-data", filename="chunks.jsonl", repo_type="dataset")
+    index_path = hf_hub_download(repo_id="Bartix84/politicbot-data", filename="faiss_index.index", repo_type="dataset")
+    metadata_path = hf_hub_download(repo_id="Bartix84/politicbot-data", filename="metadata_titles.json", repo_type="dataset")
+
+    index = faiss.read_index(index_path)
+
+    with open(metadata_path, "r", encoding="utf-8") as f:
+        metadata = json.load(f)
+
+    with open(chunks_path, "r", encoding="utf-8") as f:
+        chunks = [json.loads(line) for line in f]
+
+    return index, metadata, chunks
+
+def search_in_global_index(query_embedding, index, metadata, chunks, selected_ideology, k=5):
+    _, indices = index.search(query_embedding, k * 8)
+    results = []
+    keywords = ideology_keywords.get(selected_ideology, [])
+    seen_titles = set()
+
+    for i in range(indices.shape[1]):
+        idx = indices[0][i]
+        title = metadata[idx]
+        if title in seen_titles:
+            continue
+        seen_titles.add(title)
+        match = next((chunk for chunk in chunks if chunk["title"] == title), None)
+        if match:
+            title_text = title.lower()
+            if any(keyword in title_text for keyword in keywords):
+                results.append(match)
+        if len(results) >= k:
+            break
+    return results
+
+def generate_rag_response(ideology, user_query, context_chunks):
+    context = "\n\n".join(chunk["chunk"] for chunk in context_chunks)[:1500]
+
+    system_prompt = f"You are a political assistant who thinks and reasons like a {ideology} thinker."
+
+    user_prompt = f"""
+    Answer the following political or ethical question based strictly on the CONTEXT provided.
+    Think according to the principles and values of {ideology}. If the context is insufficient, clearly say so or explain its limitations.
+    Avoid always starting your answer the same way. Vary the introduction while staying formal and ideologically grounded.
+CONTEXT:
+{context}
+QUESTION:
+{user_query}
+ANSWER:"""
+
+    headers = {
+        "Authorization": f"Bearer {st.secrets['OPENROUTER_API_KEY']}",
+        "HTTP-Referer": "https://yourappname.streamlit.app",
+        "X-Title": "PoliticBot"
+    }
+
+    payload = {
+        "model": "mistralai/mistral-7b-instruct",
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt}
+        ],
+        "temperature": 0.9,
+        "max_tokens": 768,
+        "top_p": 0.95
+    }
+
+    response = httpx.post(
+        "https://openrouter.ai/api/v1/chat/completions",
+        headers=headers,
+        json=payload,
+        timeout=60
+    )
+
+    if response.status_code != 200:
+        return f"❌ Error {response.status_code}: {response.text}"
+
+    return response.json()["choices"][0]["message"]["content"].strip()
+
+# ---------------------- STREAMLIT INTERFACE ----------------------
+
+st.image('portada3.jpg', use_container_width=True)
+st.title('🗳️ PoliticBot')
+st.subheader('Reasoning with political ideologies')
+
+with st.sidebar:
+    st.header("Choose a political ideology")
+
+    if "selected_ideology" not in st.session_state:
+        st.session_state.selected_ideology = None
+
+    for ideology in ideology_families:
+        if st.button(ideology):
+            st.session_state.selected_ideology = ideology
+
+selected_ideology = st.session_state.selected_ideology
+
+if selected_ideology:
+    st.write(f"You have selected: **{selected_ideology}**")
+    user_query = st.text_area("Write your question or political dilemma:", height=100)
+
+    if st.button("Send question"):
+        if user_query.strip() == "":
+            st.warning("Write a question before continuing.")
+        else:
+            with st.spinner("Thinking like that ideology..."):
+                query_emb = embed_query(user_query + " in the context of " + selected_ideology)
+                index, metadata, chunks = load_data_global()
+                context = search_in_global_index(query_emb, index, metadata, chunks, selected_ideology, k=5)
+                response = generate_rag_response(selected_ideology, user_query, context)
+
+                st.subheader("🤖 Generated response:")
+                st.markdown(f"> {response}")
+
+                with st.expander("🌐 Display the context used"):
+                    if not context:
+                        st.markdown("*No relevant context found.*")
+                    else:
+                        for chunk in context:
+                            st.markdown(f"**{chunk['title']}**")
+                            st.code(chunk["chunk"][:500] + "...")