Create app.py

app.py

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+# app.py
+import os
+import io
+import json
+import re
+from datetime import datetime
+import pytz
+from typing import Optional
+
+from fastapi import FastAPI, UploadFile, File, Form, Request
+from fastapi.responses import JSONResponse, HTMLResponse
+from fastapi.templating import Jinja2Templates
+from PIL import Image
+
+# google-genai
+from google import genai
+from google.genai import types
+
+# =====================================================
+# CONFIG
+# =====================================================
+API_KEY = os.environ.get("GENAI_API_KEY", "AIzaSyCjMsYC-mDTwOr1at1-91EkMwI2O6eOvXg")
+MODEL = os.environ.get("GENAI_MODEL", "gemini-2.5-flash")
+client = genai.Client(api_key=API_KEY)
+
+# =====================================================
+# MINI-AI TOOL FUNCTIONS (unchanged behavior)
+# =====================================================
+def time_tool(location: str = "UTC") -> dict:
+    if location and "india" in location.lower():
+        tz = pytz.timezone("Asia/Kolkata")
+    else:
+        tz = pytz.utc
+    now = datetime.now(tz)
+    return {
+        "date": now.strftime("%Y-%m-%d"),
+        "time_24": now.strftime("%H:%M:%S"),
+        "time_12": now.strftime("%I:%M:%S %p"),
+        "timezone": str(tz)
+    }
+
+def date_tool(location: str = "UTC") -> dict:
+    if location and "india" in location.lower():
+        tz = pytz.timezone("Asia/Kolkata")
+    else:
+        tz = pytz.utc
+    now = datetime.now(tz)
+    return {"date": now.strftime("%A, %d-%m-%Y"), "timezone": str(tz)}
+
+def math_tool(expression: str) -> dict:
+    try:
+        allowed_names = {}
+        value = eval(expression, {"__builtins__": None}, allowed_names)
+        return {"expression": expression, "result": str(value)}
+    except Exception:
+        return {"expression": expression, "error": "Could not evaluate expression."}
+
+def weather_tool(location: str) -> dict:
+    return {"location": location, "temperature": 25, "unit": "C", "note": "dummy data; integrate a weather API for real results."}
+
+# =====================================================
+# LLM wrappers
+# =====================================================
+def generate_text(system_instruction: str, content: str) -> str:
+    cfg = types.GenerateContentConfig(system_instruction=system_instruction)
+    resp = client.models.generate_content(model=MODEL, config=cfg, contents=content)
+    return getattr(resp, "text", "").strip()
+
+def grounded_search(query: str) -> str:
+    grounding_tool = types.Tool(google_search=types.GoogleSearch())
+    cfg = types.GenerateContentConfig(tools=[grounding_tool])
+    resp = client.models.generate_content(model=MODEL, config=cfg, contents=query)
+    return getattr(resp, "text", "").strip()
+
+# =====================================================
+# Router logic (kept same as your code)
+# =====================================================
+import re as _re
+FACTUAL_KEYWORDS = _re.compile(
+    r"\b(time|date|today|now|what's the time|what is the time|weather|forecast|temperature|convert|calculate|solve|sum|add|subtract|multiply|divide|what is)\b",
+    flags=_re.I
+)
+MATH_PATTERN = _re.compile(r"^[0-9\.\s\+\-\*\/\(\)]+$")
+MATH_KEYWORDS = _re.compile(r"\b(calculate|solve|what is|evaluate|sum|add|subtract|multiply|divide)\b", flags=_re.I)
+
+def decide_tool(user_query: str) -> dict:
+    q = user_query.strip().lower()
+    if _re.search(r"\bhello\b|\bhi\b|\bhey\b|\bgood morning\b|\bgood evening\b", q):
+        return {"function_to_use": "chat", "reason": "Greeting detected by rule."}
+    if "weather" in q or "forecast" in q or "temperature" in q:
+        return {"function_to_use": "weather", "reason": "Weather-related keyword matched."}
+    if _re.search(r"\bthermostat\b|\bset thermostat\b|\bset temperature\b", q):
+        return {"function_to_use": "thermostat", "reason": "Thermostat control intent matched."}
+    if "india" in q and _re.search(r"\btime\b|\bdate\b|\bnow\b|\bcurrent\b", q):
+        if "time" in q:
+            return {"function_to_use": "time", "reason": "Explicit 'time' + 'India' matched."}
+        if "date" in q:
+            return {"function_to_use": "date", "reason": "Explicit 'date' + 'India' matched."}
+    if MATH_PATTERN.match(user_query) or (_re.search(MATH_KEYWORDS, q) and any(ch.isdigit() for ch in q)):
+        return {"function_to_use": "math", "reason": "Math expression or math keywords detected."}
+    if _re.search(FACTUAL_KEYWORDS, q):
+        if "time" in q and "india" not in q:
+            return {"function_to_use": "time", "reason": "Time query detected; using deterministic time tool."}
+        return {"function_to_use": "search", "reason": "Factual query matched; using grounded search."}
+    system_instruction = """
+You are a strict router assistant. Decide exactly one tool for this query and return only valid JSON with keys:
+{"function_to_use": "<one of: chat, search, time, date, math, weather, thermostat, science>", "reason": "short explanation"}
+Do not return anything else.
+"""
+    try:
+        resp = client.models.generate_content(model=MODEL, config=types.GenerateContentConfig(system_instruction=system_instruction), contents=user_query)
+        text = getattr(resp, "text", "").strip()
+        parsed = json.loads(text)
+        if "function_to_use" in parsed:
+            return parsed
+    except Exception:
+        pass
+    return {"function_to_use": "chat", "reason": "Default fallback to chat."}
+
+def teacher_polish(user_query: str, tool_name: str, tool_output) -> str:
+    system_instruction = (
+        "You are ICIS AI teacher. Produce a concise (1-3 sentence) explanation in teacher tone.\n"
+        "IF the tool_output contains numeric facts (dates, times, numbers), DO NOT change them; only rephrase and add a short real-life example.\n"
+        "If the tool_output is an action confirmation (like thermostat status), confirm the action succinctly.\n"
+        "Return only the final user-facing text."
+    )
+    content = f"User query: {user_query}\nTool used: {tool_name}\nTool output: {json.dumps(tool_output, ensure_ascii=False)}"
+    return generate_text(system_instruction=system_instruction, content=content)
+
+def hub_handle(user_query: str):
+    decision = decide_tool(user_query)
+    tool_name = decision.get("function_to_use", "chat")
+    tool_output = None
+    if tool_name == "time":
+        loc = "India" if "india" in user_query.lower() else "UTC"
+        tool_output = time_tool(location=loc)
+    elif tool_name == "date":
+        loc = "India" if "india" in user_query.lower() else "UTC"
+        tool_output = date_tool(location=loc)
+    elif tool_name == "math":
+        expr = _re.sub(r"[^0-9\.\+\-\*\/\(\)\s]", "", user_query).strip() or user_query
+        tool_output = math_tool(expr)
+    elif tool_name == "weather":
+        m = _re.search(r"in ([A-Za-z\s]+)$", user_query, flags=_re.I)
+        loc = m.group(1).strip() if m else "London"
+        tool_output = weather_tool(loc)
+    elif tool_name == "thermostat":
+        m = _re.search(r"(\d+)", user_query)
+        temp = int(m.group(1)) if m else 20
+        tool_output = {"status": "success", "set_to": temp}
+    elif tool_name == "search":
+        tool_output_text = grounded_search(user_query)
+        tool_output = {"search_text": tool_output_text}
+    elif tool_name == "science":
+        system_inst = "You are an ICIS science teacher; explain succinctly in 2-3 sentences with a simple example."
+        expl = generate_text(system_inst, user_query)
+        tool_output = {"explanation": expl}
+    else:
+        system_inst = "You are a friendly ICIS AI teacher, reply casually and briefly."
+        reply = generate_text(system_inst, user_query)
+        tool_output = {"reply": reply}
+    final = teacher_polish(user_query=user_query, tool_name=tool_name, tool_output=tool_output)
+    return {
+        "user_query": user_query,
+        "decision": decision,
+        "tool_output": tool_output,
+        "final_response": final
+    }
+
+# =====================================================
+# Helpers: strip markdown -> plain text, concise
+# =====================================================
+def strip_markdown(md: Optional[str]) -> str:
+    if not md:
+        return ""
+    text = str(md)
+    # remove code fences
+    text = re.sub(r"```.*?```", "", text, flags=re.S)
+    # images ![alt](url)
+    text = re.sub(r"!\[.*?\]\(.*?\)", "", text)
+    # links [text](url) -> text
+    text = re.sub(r"\[([^\]]+)\]\([^\)]+\)", r"\1", text)
+    # inline codes `x`
+    text = re.sub(r"`([^`]*)`", r"\1", text)
+    # remove remaining markdown symbols like # * > -
+    text = re.sub(r"(^|\s)[#>*\-]+\s*", r"\1", text)
+    # collapse whitespace
+    text = re.sub(r"\s+\n", "\n", text)
+    text = re.sub(r"\n{2,}", "\n\n", text)
+    text = text.strip()
+    return text
+
+def concise_text(plain: str, max_sentences: int = 2) -> str:
+    if not plain:
+        return ""
+    # naive sentence split
+    parts = re.split(r'(?<=[\.\?\!])\s+', plain.strip())
+    if len(parts) <= max_sentences:
+        return " ".join([p.strip() for p in parts]).strip()
+    return " ".join(p.strip() for p in parts[:max_sentences]).strip()
+
+# =====================================================
+# FastAPI app + endpoints
+# =====================================================
+app = FastAPI(title="ICIS Mini-Hub")
+
+templates = Jinja2Templates(directory="templates")
+
+@app.get("/", response_class=HTMLResponse)
+async def index(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/chat")
+async def chat_endpoint(payload: dict):
+    q = payload.get("query") if isinstance(payload, dict) else None
+    if not q:
+        return JSONResponse(status_code=400, content={"error": "Missing 'query' in JSON payload."})
+    out = hub_handle(q)
+    # extract final_response and function used
+    final_md = out.get("final_response", "")
+    plain = strip_markdown(final_md)
+    concise = concise_text(plain, max_sentences=2)
+    function_used = out.get("decision", {}).get("function_to_use", "chat")
+    return JSONResponse(content={
+        "function_used": function_used,
+        "response": concise
+    })
+
+@app.post("/analyze_image")
+async def analyze_image(file: UploadFile = File(...), prompt: str = Form(...)):
+    # read and ensure it's an image
+    content_type = file.content_type or ""
+    if not content_type.startswith("image/"):
+        return JSONResponse(status_code=400, content={"error": "Uploaded file is not an image."})
+    image_bytes = await file.read()
+    try:
+        image = Image.open(io.BytesIO(image_bytes))
+    except Exception:
+        return JSONResponse(status_code=400, content={"error": "Could not open image."})
+    # call genai with image + prompt
+    try:
+        response = client.models.generate_content(model=MODEL, contents=[image, prompt])
+        text_md = getattr(response, "text", "")
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": f"GenAI image analysis failed: {str(e)}"})
+    plain = strip_markdown(text_md)
+    concise = concise_text(plain, max_sentences=2)
+    return JSONResponse(content={"mode": "image", "response": concise})
+
+@app.post("/summarize_pdf")
+async def summarize_pdf(file: UploadFile = File(...), prompt: str = Form(...)):
+    ct = file.content_type or ""
+    if ct != "application/pdf":
+        return JSONResponse(status_code=400, content={"error": "Uploaded file is not a PDF."})
+    data = await file.read()
+    try:
+        part = types.Part.from_bytes(data=data, mime_type='application/pdf')
+        response = client.models.generate_content(model=MODEL, contents=[part, prompt])
+        text_md = getattr(response, "text", "")
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": f"GenAI PDF summarization failed: {str(e)}"})
+    plain = strip_markdown(text_md)
+    concise = concise_text(plain, max_sentences=2)
+    return JSONResponse(content={"mode": "pdf", "response": concise})