create app.py

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+
+def answer_question(question):
+    # This is a placeholder function. You should implement your model inference logic here.
+    # For demonstration purposes, we'll return a generic answer.
+    answers = {
+        "how to detect crop disease": "To detect crop diseases, use image recognition models trained on datasets of diseased and healthy crops.",
+        "best time to plant wheat": "The best time to plant wheat depends on your region. In temperate regions, it's usually early autumn.",
+        "improving soil fertility": "Improving soil fertility can be achieved by rotating crops, using compost, and avoiding overuse of chemical fertilizers.",
+    }
+    # Find the closest question and return the answer
+    question = question.lower()
+    for key in answers:
+        if key in question:
+            return answers[key]
+    return "I'm not sure how to answer that. Can you ask something else?"
+
+app = gr.Interface(fn=answer_question,
+                   inputs=gr.inputs.Textbox(lines=2, placeholder="Ask a question about agriculture..."),
+                   outputs="text",
+                   title="Agriculture Assistant",
+                   description="Ask any question about agriculture, and I'll try to provide an informative answer.")
+
+if __name__ == "__main__":
+    app.launch()

requirements.txt

@@ -1,4 +1,5 @@
 python-dotenv
 mistralai
 langchain-community
-faiss-cpu
+faiss-cpu
+gradio