Update README.md

README.md

@@ -4,216 +4,127 @@ datasets:
 - vidore/syntheticDocQA_artificial_intelligence_test
 - aps/super_glue
 metrics:
-- exact_match
-- f1
-- recall
-- perplexity
-- bleu
-- rouge
 - accuracy
+language:
+- en
 base_model:
 - openai-community/gpt2
 - deepseek-ai/DeepSeek-R1
-new_version: qualcomm/Stable-Diffusion-v2.1
-pipeline_tag: question-answering
-library_name: transformers
-tags:
-- code
-- finance
-- biology
-- chemistry
+new_version: deepseek-ai/Janus-Pro-7B
+library_name: diffusers
 ---
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+from flask import Flask, request, jsonify
+from transformers import pipeline
+import openai
+from newsapi import NewsApiClient
+from notion_client import Client
+from datetime import datetime, timedelta
+import torch
+from diffusers import StableDiffusionPipeline
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load Hugging Face Question-Answering model
+qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+
+# OpenAI API Key (Replace with your own)
+openai.api_key = "your_openai_api_key"
+
+# NewsAPI Key (Replace with your own)
+newsapi = NewsApiClient(api_key="your_news_api_key")
+
+# Notion API Key (Replace with your own)
+notion = Client(auth="your_notion_api_key")
+
+# Load Stable Diffusion for Image Generation
+device = "cuda" if torch.cuda.is_available() else "cpu"
+sd_model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to(device)
+
+# === FUNCTION 1: Answer Student Questions ===
+@app.route("/ask", methods=["POST"])
+def answer_question():
+    data = request.json
+    question = data.get("question", "")
+    context = "This AI is trained to assist students with questions related to various subjects."
+    
+    if not question:
+        return jsonify({"error": "Please provide a question."}), 400
+    
+    answer = qa_pipeline(question=question, context=context)
+    return jsonify({"question": question, "answer": answer["answer"]})
+
+# === FUNCTION 2: Generate Code ===
+@app.route("/generate_code", methods=["POST"])
+def generate_code():
+    data = request.json
+    prompt = data.get("prompt", "")
+    
+    if not prompt:
+        return jsonify({"error": "Please provide a prompt for code generation."}), 400
+    
+    response = openai.Completion.create(
+        engine="code-davinci-002",
+        prompt=prompt,
+        max_tokens=100
+    )
+    return jsonify({"code": response.choices[0].text.strip()})
+
+# === FUNCTION 3: Get Daily News ===
+@app.route("/news", methods=["GET"])
+def get_news():
+    headlines = newsapi.get_top_headlines(language="en", category="technology")
+    news_list = [{"title": article["title"], "url": article["url"]} for article in headlines["articles"]]
+    
+    return jsonify({"news": news_list})
+
+# === FUNCTION 4: Create a Planner Task ===
+@app.route("/planner", methods=["POST"])
+def create_planner():
+    data = request.json
+    task = data.get("task", "")
+    days = int(data.get("days", 1))
+
+    if not task:
+        return jsonify({"error": "Please provide a task."}), 400
+    
+    due_date = datetime.now() + timedelta(days=days)
+    
+    return jsonify({"task": task, "due_date": due_date.strftime("%Y-%m-%d")})
+
+# === FUNCTION 5: Save Notes to Notion ===
+@app.route("/notion", methods=["POST"])
+def save_notion_note():
+    data = request.json
+    title = data.get("title", "Untitled Note")
+    content = data.get("content", "")
+
+    if not content:
+        return jsonify({"error": "Please provide content for the note."}), 400
+    
+    notion.pages.create(
+        parent={"database_id": "your_notion_database_id"},
+        properties={"title": {"title": [{"text": {"content": title}}]}},
+        children=[{"object": "block", "type": "paragraph", "paragraph": {"text": [{"type": "text", "text": {"content": content}}]}}]
+    )
+
+    return jsonify({"message": "Note added successfully to Notion!"})
+
+# === FUNCTION 6: Generate AI Images ===
+@app.route("/generate_image", methods=["POST"])
+def generate_image():
+    data = request.json
+    prompt = data.get("prompt", "")
+
+    if not prompt:
+        return jsonify({"error": "Please provide an image prompt."}), 400
+
+    image = sd_model(prompt).images[0]
+    image_path = "generated_image.png"
+    image.save(image_path)
+    
+    return jsonify({"message": "Image generated successfully!", "image_path": image_path})
+
+# === RUN THE APP ===
+if __name__ == "__main__":
+    app.run(debug=True)