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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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from langchain_community.vectorstores import FAISS |
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from langchain_community.embeddings import HuggingFaceEmbeddings |
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from langchain_core.prompts import PromptTemplate |
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from huggingface_hub import hf_hub_download |
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import os |
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from huggingface_hub import login |
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hf_token = os.environ["HF_TOKEN"] |
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login(token=hf_token) |
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model_name = "SelmaNajih001/GRPORagMinstral2" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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pipe = pipeline( |
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"text-generation", |
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model=model_name, |
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tokenizer=model_name, |
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max_new_tokens=600, |
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temperature=0.0, |
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do_sample=False, |
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num_beams=6, |
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repetition_penalty=1.5, |
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return_full_text=False, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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prompt_template = """ |
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You are a financial market analyst. |
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Before making a prediction, you must analyze the past, provided in the Context below. |
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Your goal is to identify similar historical situations and use them to infer what may happen next. |
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Your analysis must be comprehensive, covering macroeconomic, sectoral, and corporate-specific factors. |
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You should identify past periods that closely resemble the current environment (e.g., "high inflation + geopolitical conflict" or "rate hikes + tech earnings slump"). |
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Base your reasoning on actual market reactions from those periods — specify which companies or sectors moved and how. |
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If multiple scenarios are possible, explain each one and why the market may react differently under varying conditions. |
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Explicitly name the historical reference period(s) used (e.g., "2008 financial crisis," "2020 pandemic crash and recovery," etc.). |
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Response Format: |
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-Chosen Stock or List of Stocks: |
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(name/names) |
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-Prediction(s): |
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(expected price change or direction) |
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-Explanation: |
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A concise, factual analysis linking the historical precedent to the current conditions. |
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Mention the relevant macroeconomic, sector, and corporate factors, explaining how they interacted in the past and why similar outcomes may occur again. " |
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Here the Context: |
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{context} |
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Question: |
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What could happen after: {question} |
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""" |
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prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"]) |
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embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/multi-qa-mpnet-base-dot-v1") |
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import tempfile |
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tmp_dir = tempfile.mkdtemp() |
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vectorstore = FAISS.load_local( |
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folder_path=".", |
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embeddings=embeddings, |
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allow_dangerous_deserialization=True |
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) |
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def analisi_finanziaria(query, k=3): |
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docs_found = vectorstore.similarity_search(query, k=k) |
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context = "\n".join([doc.page_content for doc in docs_found]) |
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final_prompt = prompt.format(context=context, question=query) |
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result = pipe(final_prompt)[0]['generated_text'] |
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return result |
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import gradio as gr |
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examples = [ |
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"Trump imposes tariffs", |
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"Tesla announces a new affordable electric vehicle model", |
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"Nvidia releases new GPU technology", |
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"Apple launches Apple TV+ subscription service", |
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"Elon Musk created a new political party to run against Trump" |
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] |
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description_md = """ |
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This tool analyzes financial events using a retrieval-augmented **language model**. |
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**How it works:** |
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- The model leverages historical events and news to provide predictions. |
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- For each input, similar past events are retrieved to give context. |
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- The output includes: |
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- **Chosen Stocks**: stocks likely impacted |
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- **Prediction**: expected price change |
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- **Explanation**: brief reasoning based on historical context |
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**Example use cases:** |
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- Market reactions to political events |
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- Corporate announcements and earnings reports |
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- Technological product launches |
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Click an example below to quickly test the model. |
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""" |
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iface = gr.Interface( |
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fn=analisi_finanziaria, |
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inputs=gr.Textbox(label="Enter financial event", placeholder="Type an event here..."), |
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outputs=gr.Textbox(label="Prediction"), |
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title="GRPO Financial Analyst", |
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description=description_md, |
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examples=[[e] for e in examples], |
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allow_flagging="never" |
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) |
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iface.launch() |
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