Update app.py

app.py

@@ -1,64 +1,210 @@
+import os
+import re
+import requests
+import torch
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, pipeline
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient(model="openai/gpt-oss-20b", provider="auto")
+# --- Model Setup
+MODEL_ID = "Aaayushiii/mt5-crop-lora"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 
+# Prefer GPU if available
+device = 0 if torch.cuda.is_available() else -1
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Build the pipeline (text2text for mT5)
+pipe = pipeline(
+    "text2text-generation",
+    model=MODEL_ID,
+    tokenizer=tokenizer,
+    device=device,
+)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# --- Weather (OpenWeather key or fallback)
+OPENWEATHER_API_KEY = os.getenv("OPENWEATHER_API_KEY", "").strip()
 
-    messages.append({"role": "user", "content": message})
+def get_weather(location: str) -> str | None:
+    if OPENWEATHER_API_KEY:
+        try:
+            url = (
+                "http://api.openweathermap.org/data/2.5/weather"
+                f"?q={location}&appid={OPENWEATHER_API_KEY}&units=metric"
+            )
+            r = requests.get(url, timeout=10).json()
+            if r.get("cod") == 200:
+                return f"📍 {r['name']} | 🌡️ {r['main']['temp']}°C | {r['weather'][0]['description'].capitalize()}"
+        except Exception:
+            pass
+    # Fallback Open-Meteo
+    try:
+        geo = requests.get(
+            f"https://geocoding-api.open-meteo.com/v1/search?name={location}&count=1",
+            timeout=10,
+        ).json()
+        if not geo.get("results"):
+            return None
+        lat, lon = geo["results"][0]["latitude"], geo["results"][0]["longitude"]
+        city = geo["results"][0]["name"]
+        wx = requests.get(
+            f"https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}&current_weather=true",
+            timeout=10,
+        ).json()
+        cw = wx.get("current_weather")
+        if cw:
+            return f"📍 {city} | 🌡️ {cw['temperature']}°C | 💨 {cw['windspeed']} km/h"
+    except Exception:
+        pass
+    return None
 
-    response = ""
+def extract_location(text: str) -> str | None:
+    caps = re.findall(r"\b([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\b", text)
+    return max(caps, key=len) if caps else None
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+def crop_recommendation(query: str, weather: str | None = None) -> str:
+    prompt = f"User: {query}\n"
+    if weather:
+        prompt += f"(Weather: {weather})\n"
+    prompt += "Assistant: Provide crop recommendation including top options, weather suitability, and profitability."
+    out = pipe(
+        prompt,
+        max_new_tokens=150,
+        num_beams=3,
+        do_sample=True,
+        temperature=0.7,
+    )[0]["generated_text"]
+    return out
 
-        response += token
-        yield response
+def agent(query: str) -> str:
+    loc = extract_location(query)
+    if "weather" in query.lower():
+        if not loc:
+            return "⚠️ Please specify a location."
+        return get_weather(loc) or f"⚠️ Weather not found for {loc}."
+    if loc:
+        weather = get_weather(loc)
+        return crop_recommendation(query, weather)
+    return crop_recommendation(query)
 
+# --- UI
+img_path = "assets/crop.png"
+examples = [
+    "Weather in Delhi",
+    "Best crop in Agra during Kharif",
+    "What should I sow in Maharashtra this Rabi?",
+]
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+with gr.Blocks(css="""
+    body {
+        background: linear-gradient(to right bottom, #d0f0e0, #f7fff7);
+        font-family: 'Inter', sans-serif;
+        color: #1b4332;
+        display: flex;
+        justify-content: center;
+    }
+    .main-card {
+        max-width: 800px;
+        margin: 40px auto;
+        padding: 24px;
+        background: rgba(255, 255, 255, 0.75);
+        border-radius: 20px;
+        box-shadow: 0 8px 30px rgba(0,0,0,0.12);
+        backdrop-filter: blur(12px);
+        border: 1px solid rgba(255,255,255,0.3);
+    }
+    .chatbox {
+        background: rgba(255, 255, 255, 0.65);
+        border-radius: 16px;
+        padding: 12px;
+        border: 1px solid rgba(167,201,87,0.4);
+        min-height: 350px;
+        backdrop-filter: blur(8px);
+    }
+    .gr-button {
+        background: linear-gradient(to right bottom, #6a994e, #a7c957) !important;
+        color: white !important;
+        border-radius: 14px;
+        font-weight: 600;
+        padding: 10px 18px;
+        transition: all 0.25s ease-in-out;
+        margin-top: 10px;
+    }
+    .gr-button:hover {
+        transform: translateY(-2px) scale(1.05);
+        box-shadow: 0 0 12px rgba(106,153,78,0.6);
+    }
+    .gr-textbox textarea {
+        border: 2px solid #a7c957;
+        border-radius: 14px;
+        padding: 12px;
+        font-size: 15px;
+        background: rgba(255,255,255,0.8);
+        backdrop-filter: blur(6px);
+    }
+    h1 {
+        font-size: 32px;
+        font-weight: 700;
+        margin-bottom: 20px;
+        text-align: center;
+        background: linear-gradient(to right bottom, #2f5233, #588157);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+    }
+    #example-row .examples {
+        display: flex;
+        justify-content: center;
+        flex-wrap: wrap;
+        gap: 10px;
+        margin: 15px 0 20px 0;
+    }
+    #example-row .example {
+        border-radius: 20px;
+        background: linear-gradient(to right bottom, #6a994e, #a7c957);
+        color: #fff;
+        font-size: 14px;
+        padding: 6px 16px;
+        cursor: pointer;
+        transition: all 0.25s ease;
+        box-shadow: 0 4px 12px rgba(0,0,0,0.1);
+    }
+    #example-row .example:hover {
+        background: linear-gradient(to right bottom, #588157, #6a994e);
+        transform: translateY(-2px) scale(1.06);
+        box-shadow: 0 0 15px rgba(106,153,78,0.7);
+    }
+""") as demo:
+    with gr.Column(elem_classes="main-card"):
+        gr.Markdown("<h1>🌱 AgriBot – Crop + Weather Assistant</h1>")
+        chatbot = gr.Chatbot(label="Chat with AgriBot", elem_classes="chatbox", height=400)
+        user_in = gr.Textbox(placeholder="Ask something, e.g., 'Weather in Jaipur?'")
+        gr.Examples(examples=examples, inputs=user_in, elem_id="example-row")
+        submit = gr.Button("Ask")
+
+        if os.path.exists(img_path):
+            gr.Image(img_path, show_label=False)
+        gr.Markdown("""
+        ### 🍀 Features
+        - 🌦️ Live weather lookup
+        - 🌾 Crop recommendations
+        - 💰 Profitability insights
+        """)
 
+    def respond(history, msg):
+        reply = agent(msg)
+        history.append((msg, reply))
+        return history, None
+
+    submit.click(respond, [chatbot, user_in], [chatbot, user_in])
+    user_in.submit(respond, [chatbot, user_in], [chatbot, user_in])
 
 if __name__ == "__main__":
-    demo.launch()
+    # Works both locally (no localhost access -> share link) and on Spaces
+    port = int(os.getenv("PORT", "7860"))
+    share_flag = os.getenv("GRADIO_SHARE", "true").lower() in {"1", "true", "yes"}
+    demo.queue(concurrency_count=4).launch(
+        server_name="0.0.0.0",
+        server_port=port,
+        share=share_flag,
+        show_error=True,
+    )
+
+