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app.py

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+# ===============================
+# 1️⃣ Install dependencies (only in Colab, HF Space installs from requirements.txt)
+# ===============================
+# !pip install -q groq datasets sentence-transformers faiss-cpu gradio matplotlib pandas tqdm reportlab
+
+# ===============================
+# 2️⃣ Imports
+# ===============================
+import os
+import faiss
+import numpy as np
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+import datetime
+from io import BytesIO
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+from reportlab.lib.utils import ImageReader
+
+# ===============================
+# 3️⃣ Groq Client
+# ===============================
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# ===============================
+# 4️⃣ Load datasets for RAG
+# ===============================
+medical_ds = load_dataset("lavita/medical-qa-datasets", "all-processed", split="train[:1000]")
+stress_ds = load_dataset("Amod/mental_health_counseling_conversations", split="train[:500]")
+
+# ===============================
+# 5️⃣ Prepare documents
+# ===============================
+documents = []
+for row in medical_ds:
+    instr = row.get("instruction","") or ""
+    inp = row.get("input","") or ""
+    out = row.get("output","") or ""
+    text = instr.strip()
+    if inp.strip(): text += " " + inp.strip()
+    text += " " + out.strip()
+    documents.append(text)
+for row in stress_ds:
+    context = row.get("Context","") or ""
+    response = row.get("Response","") or ""
+    documents.append(context + " " + response)
+
+# ===============================
+# 6️⃣ Embeddings + FAISS
+# ===============================
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+embeddings = embedder.encode(documents, convert_to_numpy=True, show_progress_bar=True)
+dimension = embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(embeddings)
+
+# ===============================
+# 7️⃣ RAG functions
+# ===============================
+def retrieve_docs(query,k=5):
+    query_embedding = embedder.encode([query])
+    distances, indices = index.search(query_embedding,k)
+    return [documents[i] for i in indices[0]]
+
+def rag_answer(query):
+    retrieved = retrieve_docs(query)
+    context = "\n\n".join(retrieved)
+    prompt = f"""
+You are a medical assistant.
+Use ONLY the context below to answer.
+Do NOT diagnose anyone.
+Provide supportive and informative responses.
+
+Context:
+{context}
+
+Question:
+{query}
+"""
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[{"role":"user","content":prompt}],
+    )
+    return response.choices[0].message.content
+
+# ===============================
+# 8️⃣ CSV persistence
+# ===============================
+CSV_FILE = "daily_entries.csv"
+if os.path.exists(CSV_FILE):
+    df = pd.read_csv(CSV_FILE, parse_dates=["date"])
+else:
+    df = pd.DataFrame(columns=["date","user_id","stress","mood","sleep_hours"])
+
+def add_daily_entry(user_id, stress, mood, sleep_hours):
+    global df
+    today = datetime.date.today()
+    new_row = pd.DataFrame([{
+        "date": today,
+        "user_id": user_id,
+        "stress": stress,
+        "mood": mood,
+        "sleep_hours": sleep_hours
+    }])
+    df = pd.concat([df,new_row], ignore_index=True)
+    df.to_csv(CSV_FILE,index=False)
+    return f"Entry for {today} saved!"
+
+# ===============================
+# 9️⃣ Weekly report + LLaMA + chart
+# ===============================
+def generate_weekly_report(user_id):
+    global df
+    df['date'] = pd.to_datetime(df['date'])
+    user_df = df[df['user_id']==user_id]
+    if user_df.empty:
+        return "No data available yet.", None, None
+    user_df['week'] = user_df['date'].dt.isocalendar().week
+
+    weekly_summary = user_df.groupby('week').agg({
+        "stress":["mean","max"],
+        "mood":["mean","min"],
+        "sleep_hours":["mean","min"]
+    })
+    weekly_summary['stress_change'] = weekly_summary['stress']['mean'].diff()
+    weekly_summary['mood_change'] = weekly_summary['mood']['mean'].diff()
+    weekly_summary['sleep_change'] = weekly_summary['sleep_hours']['mean'].diff()
+
+    # Charts
+    fig, ax = plt.subplots(3,1,figsize=(8,10))
+    weekly_summary['stress']['mean'].plot(ax=ax[0],title="Weekly Avg Stress",color='red',marker='o')
+    weekly_summary['mood']['mean'].plot(ax=ax[1],title="Weekly Avg Mood",color='blue',marker='o')
+    weekly_summary['sleep_hours']['mean'].plot(ax=ax[2],title="Weekly Avg Sleep Hours",color='green',marker='o')
+    plt.tight_layout()
+    chart_buf = BytesIO()
+    plt.savefig(chart_buf, format="png")
+    chart_buf.seek(0)
+
+    # LLaMA explanation
+    trend_prompt = f"""
+You are a wellness data analyst AI.
+Here is the weekly summary for user {user_id}:
+{weekly_summary.tail(4)}
+
+Explain in plain language the trends in stress, mood, and sleep over the past 4 weeks.
+"""
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[{"role":"user","content":trend_prompt}]
+    )
+    explanation = response.choices[0].message.content
+
+    # Generate PDF
+    pdf_buf = BytesIO()
+    c = canvas.Canvas(pdf_buf, pagesize=letter)
+    width, height = letter
+    c.setFont("Helvetica",12)
+    y = height - 40
+    c.drawString(30,y,f"Weekly Trend Report for User {user_id}")
+    y -= 30
+    for line in explanation.split("\n"):
+        c.drawString(30,y,line)
+        y -= 15
+        if y < 100:
+            c.showPage()
+            y = height - 40
+    # Add chart
+    img = ImageReader(chart_buf)
+    c.showPage()
+    c.drawImage(img,50,150,width=500,height=400)
+    c.save()
+    pdf_buf.seek(0)
+
+    return explanation, chart_buf, pdf_buf
+
+# ===============================
+# 🔟 Gradio interface
+# ===============================
+with gr.Blocks() as demo:
+    gr.Markdown("# 🧠 Medical & Stress RAG Assistant with Persistent Reports and PDF Export")
+
+    with gr.Tab("Daily Entry"):
+        gr.Markdown("Enter daily stress, mood, and sleep hours.")
+        stress = gr.Slider(0,10,label="Stress Level")
+        mood = gr.Slider(0,10,label="Mood Level")
+        sleep = gr.Number(label="Sleep Hours")
+        submit = gr.Button("Save Entry")
+        output_entry = gr.Textbox(label="Status")
+        submit.click(add_daily_entry,[gr.Number(value=1,label="User ID"),stress,mood,sleep],output_entry)
+
+    with gr.Tab("Weekly Trend Report"):
+        gr.Markdown("View weekly summary, trends, and export PDF.")
+        user_id_input = gr.Number(value=1,label="User ID")
+        report_output = gr.Textbox(label="Weekly Trend Explanation")
+        chart_output = gr.Image(label="Trend Chart")
+        pdf_output = gr.File(label="Download PDF")
+        generate = gr.Button("Generate Report")
+        generate.click(generate_weekly_report,[user_id_input],[report_output,chart_output,pdf_output])
+
+    with gr.Tab("Medical QA"):
+        gr.Markdown("Ask questions about stress, mood, sleep, or general wellness.")
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox(label="Your Question")
+        clear = gr.Button("Clear Chat")
+        def respond(message,history):
+            answer = rag_answer(message)
+            history.append((message,answer))
+            return "",history
+        msg.submit(respond,[msg,chatbot],[msg,chatbot])
+        clear.click(lambda: None,None,chatbot,queue=False)
+
+demo.launch()

requirements.txt

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+gradio
+faiss-cpu
+sentence-transformers
+datasets
+pandas
+matplotlib
+tqdm
+groq
+reportlab