Upload app.py

app.py

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+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+import gradio as gr
+from huggingface_hub import login
+import os
+
+login(token=os.getenv("HF_TOKEN"))
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "ranggafermata/Fermata"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32)
+
+app = FastAPI()
+
+def chat_function(message):
+    inputs = tokenizer(message, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=128)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+@app.post("/chat")
+async def chat_api(request: Request):
+    try:
+        body = await request.json()
+        prompt = body.get("input", "")
+        if not prompt:
+            return JSONResponse(content={"error": "Missing input"}, status_code=400)
+        output = chat_function(prompt)
+        return JSONResponse(content={"output": output})
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+
+# Weather API
+def get_weather(location):
+    key = os.getenv("OPENWEATHER_API_KEY")
+    if not key:
+        return "Missing API key for weather."
+    try:
+        url = f"http://api.openweathermap.org/data/2.5/weather?q={location}&appid={key}&units=metric"
+        r = requests.get(url).json()
+        return f"{r['name']}: {r['main']['temp']}°C, {r['weather'][0]['description']}"
+    except:
+        return "Failed to fetch weather."
+
+# NASA API
+def get_apod():
+    key = os.getenv("NASA_API_KEY")
+    if not key:
+        return "Missing API key for NASA."
+    try:
+        r = requests.get(f"https://api.nasa.gov/planetary/apod?api_key={key}").json()
+        return f"📷 {r['title']}\n\n{r['explanation']}\n\nMedia: {r['url']}"
+    except:
+        return "Failed to fetch NASA APOD."
+
+# Parse tool call JSON inside [TOOL_CALL] {...}
+def parse_tool_call(output):
+    if not output or "[TOOL_CALL]" not in output:
+        return None
+
+    match = re.search(r"\[TOOL_CALL\]\s*(\{.*?\})", output, re.DOTALL)
+    if not match:
+        return None
+
+    json_str = match.group(1).strip()
+    if not json_str or json_str in ["null", "None", ""]:
+        return None
+
+    try:
+        return json.loads(json_str)
+    except json.JSONDecodeError as e:
+        print(f"❌ JSON parsing failed: {e}")
+        print(f"⚠️ Bad JSON string: {json_str}")
+        return None
+
+
+# Chat logic
+def respond(message):
+    prompt = f"### Human:\n{message}\n\n### Assistant:"
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    reply = result.split("### Assistant:")[-1].strip()
+
+
+    tool = parse_tool_call(reply)
+    if tool:
+        if tool["tool"] == "get_weather":
+            return get_weather(tool.get("location", "Unknown"))
+        elif tool["tool"] == "get_apod":
+            return get_apod()
+        else:
+            return f"Tool not recognized: {tool['tool']}"
+    return reply
+
+# UI
+gr.Interface(
+    fn=respond,
+    inputs=gr.Textbox(lines=2, placeholder="Ask me something..."),
+    outputs="text",
+    title="Fermata AI 1.0",
+    description="Now powered by the official Gemma 3 model. Ask about the weather or NASA's daily space image!",
+).launch()